data analysis techniques for dissertation

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:

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:

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!

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:

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;

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.

What data collection best suits your research?

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.

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.

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:

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.

Our writers have years of experience in writing flawless and to the point mixed methods-based dissertations to be confident that the dissertation they write for you will be according to the technical requirements and the formatting guidelines.

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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:

What is a quantitative research method?

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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How to Analyse Secondary Data for a Dissertation

Secondary data refers to data that has already been collected by another researcher. For researchers (and students!) with limited time and resources, secondary data, whether qualitative or quantitative can be a highly viable source of data. In addition, with the advances in technology and access to peer reviewed journals and studies provided by the internet, it is increasingly popular as a form of data collection. The question that frequently arises amongst students however, is: how is secondary data best analysed?

The process of data analysis in secondary research

Secondary analysis (i.e., the use of existing data) is a systematic methodological approach that has some clear steps that need to be followed for the process to be effective. In simple terms there are three steps:

Let’s look at each of these in more detail:

Step One: Development of research questions

Using secondary data means you need to apply theoretical knowledge and conceptual skills to be able to use the dataset to answer research questions. Clearly therefore, the first step is thus to clearly define and develop your research questions so that you know the areas of interest that you need to explore for location of the most appropriate secondary data.

Step Two: Identification of Dataset

This stage should start with identification, through investigation, of what is currently known in the subject area and where there are gaps, and thus what data is available to address these gaps. Sources can be academic from prior studies that have used quantitative or qualitative data, and which can then be gathered together and collated to produce a new secondary dataset. In addition, other more informal or “grey” literature can also be incorporated, including consumer report, commercial studies or similar. One of the values of using secondary research is that original survey works often do not use all the data collected which means this unused information can be applied to different settings or perspectives.

Key point: Effective use of secondary data means identifying how the data can be used to deliver meaningful and relevant answers to the research questions. In other words that the data used is a good fit for the study and research questions.

Step Three: Evaluation of the dataset for effectiveness/fit

A good tip is to use a reflective approach for data evaluation. In other words, for each piece of secondary data to be utilised, it is sensible to identify the purpose of the work, the credentials of the authors (i.e., credibility, what data is provided in the original work and how long ago it was collected). In addition, the methods used and the level of consistency that exists compared to other works. This is important because understanding the primary method of data collection will impact on the overall evaluation and analysis when it is used as secondary source. In essence, if there is no understanding of the coding used in qualitative data analysis to identify key themes then there will be a mismatch with interpretations when the data is used for secondary purposes. Furthermore, having multiple sources which draw similar conclusions ensures a higher level of validity than relying on only one or two secondary sources.

A useful framework provides a flow chart of decision making, as shown in the figure below.

Following this process ensures that only those that are most appropriate for your research questions are included in the final dataset, but also demonstrates to your readers that you have been thorough in identifying the right works to use.

Writing up the Analysis

Once you have your dataset, writing up the analysis will depend on the process used. If the data is qualitative in nature, then you should follow the following process.

Pre-Planning

Structure of the Analysis Presentation

Introduction.

The introduction should commence with an overview of all your sources. It is good practice to present these in a table, listed chronologically so that your work has an orderly and consistent flow. The introduction should also incorporate a brief (2-3 sentences) overview of the key outcomes and results identified.

The body text for secondary data, irrespective of whether quantitative or qualitative data is used, should be broken up into sub-sections for each argument or theme presented. In the case of qualitative data, depending on whether content, narrative or discourse analysis is used, this means presenting the key papers in the area, their conclusions and how these answer, or not, your research questions. Each source should be clearly cited and referenced at the end of the work. In the case of qualitative data, any figures or tables should be reproduced with the correct citations to their original source. In both cases, it is good practice to give a main heading of a key theme, with sub-headings for each of the sub themes identified in the analysis.

Do not use direct quotes from secondary data unless they are:

All results sections, regardless of whether primary or secondary data has been used should refer back to the research questions and prior works. This is because, regardless of whether the results back up or contradict previous research, including previous works shows a wider level of reading and understanding of the topic being researched and gives a greater depth to your own work.

Summary of results

The summary of the results section of a secondary data dissertation should deliver a summing up of key findings, and if appropriate a conceptual framework that clearly illustrates the findings of the work. This shows that you have understood your secondary data, how it has answered your research questions, and furthermore that your interpretation has led to some firm outcomes.

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

Dissertation Data Analysis Help Tailored To Meet Your Dissertation Objectives

At help with dissertation, we believe you should seek professional dissertation data analysis help from the first stage of writing the dissertation. You may ask why it is necessary to engage our dissertation data analysts. Data analysis is not an afterthought in writing a dissertation, research paper, thesis or action research paper. When formulating research hypothesis and questions, you should have in mind data analysis procedures in mind. A professional SPSS data analysis service will guide you in developing research objectives, hypothesis and questions that can supported by the most appropriate data.

Moreover, dissertation committees critically evaluate the way results have been analyzed and presented. Therefore, your dissertation can be rendered useless if major flaws are detected in your analysis. To avoid such embarrassing moments, you need to seek dissertation data analysis help form our data analysis experts for dissertations, thesis and research paper.

What is Data Analysis?

Did you know data analysis is torturing data to reveal vital information? In present world, organizations are building their future on data. All industry players, from insurance, banking, hospitality, agriculture and manufacturing are turning to data for valuable information to gain competitive advantage over their peers. Aided by advanced computing power and databases, it is possible to collect and collate large amounts of data. However, without data analysis, raw data is no use. Data analysis plays a key role in extracting valuable information from the data. Data analysis uses analytic methods both logical and statistical to draw inferences and predict the future. This systematic procedure also applies when writing a dissertation or thesis or capstone project.

Data analysis is majorly segmented into two core areas.

Our data analysis help is tailored to assist you extract as much information as possible from your research data. Our dissertation statistical consultants are conversant with both quantitative and qualitative methods of data analysis.

Data Analysis Plan for Dissertation

Are you looking for dissertation data analysis help ? Then you must know how to write a data analysis plan for your dissertation data. However, if you are unable to develop a solid data analysis plan, you can always talk to our data analysis help service for assistance.

What is a data analysis plan ? It is a roadmap detailing how data collected will be stored, cleaned transformed and thereafter analyzed. A good data analysis plan contains the following aspects

Why is Data Analysis Important in Academic Projects?

Just like how data analysis plays a major role in providing powerful insights for businesses and governments, data analysis assists scholars and researchers gain new insights. For instance, a student writing DNP project on how determine effectiveness of an improved clinical practice on patients will use data analysis to understand if any changes in patient response to treatment is due to chance or efficacy of the practice. Therefore, students undertaking academic research project will need data analysis help to render their results valid and scientifically accepted.

Three major steps to be followed when writing quantitative dissertation

There are three major steps involved during data analysis process. This includes:

Though the steps may seem easy and straightforward, they are daunting for disastrous, more so to students that lack sound quantitative background. Our data analysis help will hold your hand through the three steps of data analysis process.

Most Common Statistical Tests Utilized In Our Data Analysis Help

Most dissertations and thesis rely on quantitative data analysis to analyze data and test hypothesis. Below is a list of the common statistical tests that our dissertation data analysis help utilize:

7 Steps of Data Analysis To Be Followed By Our Dissertation Data Analysts

When turning data into actionable information, our data analysts follow seven steps to complete the data analysis process.

Hire Dissertation Data Analysts Today For Professional Data Analysis Help

help with data analysis for dissertation, thesis and research paper

We start by reviewing your research objectives, questions and hypothesis to ensure that they are compatible with the data and will fully provide statistically sound results. We will then review your dissertation methodology section to ensure that the research strategy adopted is compatible with the study’s objectives and will have minimal statistical errors. When writing dissertation methodology section it is important to seek dissertation data analysis help more so if the study is quantitative. In this chapter you’re required to highlight the data analysis approach you intend to use to achieve the research aims. Remember that statistical analysis methods are not chosen haphazardly but are backed by sound statistical reasoning.

Our SPSS dissertation data analysis service will take you through the entire analysis cycle that includes:

In case we find any mistakes we will gladly guide you and revise your work to ensure it meets the appropriate conditions. Moreover, we will offer you continued support until your dissertation has been accepted by the committee.

How Can Our SPSS Data Analysis Service For Dissertation Assist You

statistical analysis for dissertation data

However the most user friendly and commonly used data analysis software for dissertation is SPSS. The program can perform both simple and highly complex statistical analysis with minimal or no coding, making it appropriate for statistical analysis for dissertations , research papers, thesis and capstone project.

Our dissertation data analysis help include conducting statistical tests for univariate, bivariate and multivariate data. We are experts in the following statistical methods

Due to the wide application of statistics in diverse fields, our professional SPSS data analysis services are used by students writing dissertations in psychology , nursing, marketing, GIS, agriculture, IT among others.

How to Use SPSS to Analyze Dissertation Data: A Step-by-Step Guide

Imagine you’re a student working on your capstone project or dissertation. You’ve gathered a lot of data, but now you’re stuck on how to analyze it. It can be overwhelming and confusing, but fear not! This article will guide you through the process of using SPSS (Statistical Package for the Social Sciences) to analyze your dissertation data.

From here on out, we’ll be taking a journey together to learn how to use SPSS to analyze your dissertation data. Whether you’re a beginner or an advanced user, we’ve got you covered. So, sit tight, grab your favorite beverage, and let’s dive in!

Step #1: Importing Your Data to SPSS

The first step in using SPSS to analyze your dissertation data is importing it into the software. To do this, you’ll need to have your data in a compatible format, such as an Excel or CSV file.

To import your data into SPSS, select “File” from the menu bar and then “Open.” Browse to your data file and select it. You’ll then see a window with various import options. Ensure that you select the correct options for your data, such as delimiter settings and variable types.

How to import data to SPSS: First you will click file then select open and click it. It will open a dialogue box from which you can select your data file.

Step #2: Cleaning Your Data

Once you’ve imported your data, you’ll want to clean it up by removing any errors or inconsistencies. This process is essential to ensure that your results are accurate and reliable.

To clean your data in SPSS, you’ll want to use tools such as “Sort Cases” and “Select Cases” to filter out any problematic data points. You can also use the “Recode” function to transform your data into a more usable format.

How to clean data using SPSS. In this SPSS operation you can transpose, recode, merge or sort data to usable format

Step #3: Running Descriptive Statistics

With your data imported and cleaned, you’re now ready to start running statistical analyses. The first step in this process is to generate descriptive statistics for your variables.

To do this in SPSS, select “Analyze” from the menu bar and then “Descriptive Statistics.” From here, you can select the variables you want to analyze and choose which descriptive statistics you want to calculate, such as means, standard deviations, and ranges.

How to generate descriptive statistics: To do this in SPSS, click “Analyze” from the menu bar and then “Descriptive Statistics. Select the variables you want to analyze and choose which descriptive statistics you want to calculate, such as means, standard deviations, and ranges

Step #4: Running Inferential Statistics

With your descriptive statistics in hand, you can now move on to running inferential statistics. These types of analyses allow you to draw conclusions about your data and test hypotheses.

To run inferential statistics in SPSS, you’ll want to select “Analyze” from the menu bar and then choose the appropriate statistical test for your data. Common examples include t-tests, ANOVA, and regression analysis.

How to generate inferential statistics using SPSS

How to generate inferential statistics: To do this in SPSS, Click “Analyze” from the menu bar and then choose the appropriate statistical test for your analysis.

Step #5: Interpreting Your Results

Once you’ve run your statistical analyses, you’ll need to interpret your results to make sense of your data. This process can be challenging, especially for beginners, but it’s essential to ensure that your conclusions are accurate.

To interpret your results in SPSS, you’ll want to review the output generated by the software. This output includes tables, graphs, and other visual aids that can help you understand your data better. You may also want to consult with your supervisor or a statistical expert to help you interpret your results correctly.

And Finally!

Analyzing dissertation data using SPSS may seem daunting at first, but with the right guidance, it’s a manageable task. You’ll be well on your way to generating accurate and reliable results for your dissertation or capstone project. Remember to keep your data clean and well-organized, choose the appropriate statistical tests, and interpret your results carefully. You can hire our dissertation data analysis help for detailed analysis and writing assistance of results and finding chapter.

By seeking dissertation data analysis help from help with dissertation you will have less stress and anxiety when facing the dissertation committee. With our high quality statistical analysis for dissertation you will face few criticism and rewrites from the committee, making your dissertation writing a success.

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How to Use Quantitative Data Analysis for a Strong Thesis Statement

Professionals who aspire to ascend to the C-suite or other executive positions often decide to return to school to enhance their academic qualifications. Earning a doctoral degree based on quantitative data analysis is an excellent way to accomplish these goals.

While earning a doctoral degree, you will be required to write a dissertation. A dissertation is a book-length manuscript that explains the problem addressed, processes and findings of original research you conduct. One of the steps you’ll take to complete your dissertation is defining a research topic and writing a strong thesis statement to clearly explain the particular focus of your research. This guide discusses the application of quantitative data analysis to your thesis statement.

Writing a Strong Thesis Statement

In a relatively short essay of 10 to 15 pages, the thesis statement is generally found in the introductory paragraph. This kind of thesis statement is also typically rather short and straightforward. For example, if you’re writing a paper on the differences between corporate charitable donation strategies, your thesis statement might read something like this: It is not known what the differences in charitable donation strategies are in four U.S. corporations.

For a lengthy dissertation, however, the thesis statement may be found throughout the entire introduction or first chapter of the dissertation. You’ll also use your thesis statement in your dissertation proposal.

A dissertation proposal is a 70 to 150 page paper that outlines the research you intend to undertake, the methods you’ll employ to conduct it, and the questions you plan to answer or theories you wish to test. The purpose of a dissertation proposal is to convince your dissertation committee and department to approve your chosen dissertation topic. Although you should have a preliminary idea of your thesis statement when you write your proposal, keep in mind that you may refine it over time. In other words, your thesis statement might look quite different when you finish your dissertation than when you first wrote your proposal, and that’s perfectly fine.

Understanding Quantitative Data Analysis

Quantitative data analysis may sound like a mouthful, but it’s actually quite simple. It refers to the statistical analysis of numerical data. Thus, it contrasts with qualitative data analysis, which refers to the analysis of non-numerical data.

Note that it’s possible to conduct a quantitative analysis of qualitative data; however, you must first convert such qualitative data into numerical form without losing their meaning. For instance, if you’re studying the effects of various colors of wall paint on office workers’ productivity, you might code the color orange ‘1’, the color yellow ‘2’ and so on. You would then be able to perform a quantitative analysis.

All doctoral students who are completing a quantitative-based degree program will conduct quantitative research. This type of data analysis is useful for the following types of research:

Before you can write a strong thesis statement for your dissertation, you’ll need to know exactly what you plan to study and which questions you hope to answer through your research. Your thesis statement should also acknowledge your use of quantitative research methods.

A Quick Look at Quantitative Research Methods

Although your main thesis statement will likely include just a few sentences, you’ll need to provide supporting details. When writing your dissertation proposal, you’ll also need to offer some basic information about the quantitative research methods you plan to use for your work. Similarly, when writing your introduction, you will need to explain how you conducted your research and how you completed your quantitative data analysis because these crucial details will substantiate your main thesis statement.

Here’s a quick look at the main types of quantitative research methods :

After you have conducted your research and analyzed your findings, you can compare them to the original thesis statement you outlined in your dissertation proposal. From there, you can reflect on your quantitative data analysis and compare your findings to those of other researchers.

Applying Quantitative Data Analysis to Your Thesis Statement

It’s difficult—if not impossible—to flesh out a thesis statement before beginning your preliminary research. If you’re at the beginning stages of your dissertation process and are working to develop your dissertation proposal, you will first need to conduct a brief but broad literature review. You’ll conduct a more in-depth literature review after your topic is approved.

Based on your findings from the extant literature, you can begin to formulate your own original ideas regarding your topic. For instance, let’s say your dissertation focuses on the ways in which secondary school athletics affect students. Scholars have already produced much research about the benefits of sports for students, but you might notice research gaps in certain areas of the field. For example, what effects do sports have on students after graduation? Do years in sports relate to amount of soft skills in students?

You can begin to shape your thesis statement based on the questions that arise during your preliminary literature review. For instance, you may find existing research that indicates high school sports teach students to work cooperatively and communicate effectively with their peers.

Of course, because you’re writing a quantitative, data-driven dissertation, you will need to express these ideas numerically. Therefore, your thesis statement might look like this: “High school students who play sports are more likely to develop teamwork skills and develop solid communication abilities than high school students who do not play sports. My dissertation research will examine if these benefits persist long after students graduate.” As the above discussion and examples demonstrate, the key to writing a strong thesis statement is to substantiate your assertions with concrete statistics using your own quantitative data analysis.

Grand Canyon University’s College of Doctoral Studies is pleased to offer a wide variety of doctorate degrees, including the Doctor of Education in Organizational Leadership: Health Care Administration (Quantitative Research) degree, the Doctor of Business Administration: Data Analytics (Quantitative Research) program and more. Click on Request Info above to begin planning your doctoral education today.

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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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) Expert Reviewed By: Eunice Rautenbach (D.Tech) | May 2020

Qualitative data analysis methods. Wow, that’s a mouthful.

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much terminology, so many abstract, fluffy concepts. It can be a minefield!

Fear not – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project!

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s take a step back and ask the question, “what exactly is qualitative data?”.

Well, qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

qualitative data analysis vs quantitative data analysis

So, qualitative analysis is easier than quantitative, right?

Well…. not quite . In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work!

Qualitative data can be challenging and time-consuming to analyse and interpret. You’ll likely extensive or audio-based data to work through.

In this post, we will explore qualitative data analysis by looking at the general methodological approaches used for dealing with qualitative data. We’re not going to cover every possible qualitative approach and we’re not going to go into heavy detail – we’re just going to give you the big picture. These approaches can be used on primary data (that’s data you’ve collected yourself) or secondary data (data that’s already been published by someone else).

Without further delay, let’s get into it.

The Qualitative Data Analysis Methods “Big 6”

There are many different types of qualitative data analysis (QDA for short), all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each one.

Let’s take a look at them…

Need a helping hand?

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its drawbacks, so don’t be put off by these – just be aware of them !

Content analysis is used to evaluate patterns within a piece of content or across multiple pieces of content or sources of communication

QDA Method #2: Narrative Analysis

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . In other words, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , just like all analysis methods. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Narrative analysis is all about listening to people telling stories to gain insights into the ways that people deal with and make sense of reality.

QDA Method #3: Discourse Analysis

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place in. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication is important. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time consuming as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method.

Discourse analysis is all about analysing language such as a conversation or a speech, within the culture and society it takes place in.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

Thematic analysis takes bodies of data and groups them according to similarities (themes), which help us make sense of the content.

QDA Method #5: Grounded theory (GT)

Grounded Theory is powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions .” For example, you could try to develop a theory about what factors influence students to read watch a YouTube video about qualitative analysis… The important thing with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name)…

In Grounded Theory , you start with a general overarching question about a given population – for example, graduate students. Then you begin to analyse a small sample – for example, five graduate students in a department at a university. Ideally, this sample should be reasonably representative of the broader population. You’d then interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general hypothesis or pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern or this hypothesis holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory develops . What’s important with grounded theory is that the theory develops from the data – not from some preconceived idea. You need to let the data speak for itself .

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to Grounded Theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

Grounded theory is used to create a new theory (or theories) by using the data at hand, as opposed to existing theories and frameworks.

QDA Method #6:

Interpretive phenomenological analysis (ipa).

Interpretive. Phenomenological. Analysis. IPA .

Try saying that three times fast… Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” or phenomena that makes up the “P” in IPA. These phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results.

For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

Keep these limitations and pitfalls in mind though, and you’ll have a powerful analysis tool in your arsenal!

IPA can help you understand the personal experiences of a person or group concerning a major life event, an experience or a situation.

How to choose the right analysis method

Now, you’re probably asking yourself the question, “how do you choose the right one?”

Well, selecting the right qualitative analysis method largely depends on your research aims , objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

As you can see, all these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. Also, remember that each method has its own strengths, weaknesses and general limitations. No single analysis method is perfect . Therefore, it often makes sense to adopt more than one method (this is called triangulation ), but this is, of course, quite time-consuming.

As we’ve seen, these approaches all make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s really important to come into your research with a clear intention before you start thinking about which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

No single analysis method is perfect, so it can often make sense to adopt more than one method (this is called triangulation).

Let’s recap on QDA methods…

In this post, we looked at the six most popular qualitative data analysis methods, namely:

Of course, these aren’t the only approaches to qualitative data analysis, but they’re a great starting point if you’re just dipping your toes into qualitative research for the first time.

Psst… there’s more (for free)

This post is part of our research writing mini-course, which covers everything you need to get started with your dissertation, thesis or research project.

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73 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

Thanks madam . It is very important .

thank you very good

This has been very well explained in simple language . It is useful even for a new researcher.

Great to hear that. Good luck with your qualitative data analysis, Pramod!

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

very informative sequential presentation

Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Thank you, this is well explained and very useful.

Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

The session was very helpful and insightful. Thank you

This was very helpful and insightful. Easy to read and understand

As a professional academic writer, this has been so informative and educative. Keep up the good work Grad Coach you are unmatched with quality content for sure.

Keep up the good work Grad Coach you are unmatched with quality content for sure.

Its Great and help me the most. A Million Thanks you Dr.

It is a very nice work

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

This is Amazing and well explained, thanks

great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

This was incredibly helpful.

Very helpful.

very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

This was helpful. Thank you

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

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How do I make a data analysis for my bachelor, master or PhD thesis?

A data analysis is an evaluation of formal data to gain knowledge for the bachelor’s, master’s or doctoral thesis. The aim is to identify patterns in the data, i.e. regularities, irregularities or at least anomalies.

Data can come in many forms, from numbers to the extensive descriptions of objects. As a rule, this data is always in numerical form such as time series or numerical sequences or statistics of all kinds. However, statistics are already processed data.

Data analysis requires some creativity because the solution is usually not obvious. After all, no one has conducted an analysis like this before, or at least you haven't found anything about it in the literature.

The results of a data analysis are answers to initial questions and detailed questions. The answers are numbers and graphics and the interpretation of these numbers and graphics.

What are the advantages of data analysis compared to other methods?

What are the disadvantages of data analysis?

Under what conditions can or should I conduct a data analysis?

How do I create the evaluation design for the data analysis?

The most important thing is to ask the right questions, enough questions and also clearly formulated questions. Here are some techniques for asking the right questions:

Good formulation: What is the relationship between Alpha and Beta?

Poor formulation: How are Alpha and Beta related?

Now it’s time for the methods for the calculation. There are dozens of statistical methods, but as always, most calculations can be done with only a handful of statistical methods.

It depends on the media, the questions and the methods I want to use.

A fixed rule is that I need at least 30 data sets for a statistical analysis in order to be able to make representative statements about the population. So statistically it doesn't matter if I have 30 or 30 million records. That's why statistics were invented...

What mistakes do I need to watch out for?

Which tools can I use?

You can use programs of all kinds for calculations. But asking questions is your most powerful aide.

Who can legally help me with a data analysis?

The great intellectual challenge is to develop the research design, to obtain the data and to interpret the results in the end.

Am I allowed to let others perform the calculations?

That's a thing. In the end, every program is useful. If someone else is operating a program, then they can simply be seen as an extension of the program. But this is a comfortable view... Of course, it’s better if you do your own calculations.

A good compromise is to find some help, do a practical calculation then follow the calculation steps meticulously so next time you can do the math yourself. Basically, this functions as a permitted training. One can then justify each step of the calculation in the defense.

What's the best place to start?

Clearly with the detailed questions and hypotheses. These two guide the entire data analysis. So formulate as many detailed questions as possible to answer your main question or research question. You can find detailed instructions and examples for the formulation of these so-called detailed questions in the Thesis Guide.

How does the Aristolo Guide help with data evaluation for the bachelor’s or master’s thesis or dissertation?

The Thesis Guide or Dissertation Guide has instructions for data collection, data preparation, data analysis and interpretation. The guide can also teach you how to formulate questions and answer them with data to create your own experiment. We also have many templates for questionnaires and analyses of all kinds. Good luck writing your text! Silvio and the Aristolo Team PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor or master thesis in 31 days.

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Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on November 30, 2022.

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

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

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

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

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

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

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

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

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

What can proofreading do for your paper?

Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words, and awkward phrasing.

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

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

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

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

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

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

Standardizing procedures

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

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

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

Creating a data management plan

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

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

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

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

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Faculty , Graduate Students

Dissertation Defense: Jian Zou

"Clustering, Biomarker and Cancer Model Selection Using Omics Data" - Public Health/Biostatistics,

Committee: George Tseng (advisor and committee chair) Jiebiao Wang, Department of Biostatistics Wei Chen, Department of Pediatrics, School of Medicine Adrian V. Lee, Department of Pharmacology & Chemical Biology, School of Medicine

Abstract: Central dogma reforms biomedical science. Since then, biomedical researchers have focused mostly on the relationship between DNA, RNA, and protein. To quantify their sequence, structure, and abundance, numerous biotechnologies have been created. High-throughput technologies, which emerged since 2000s, offer researchers a fantastic opportunity to thoroughly grasp the mechanism of diseases and also bring many statistical challenges. This proposal focuses on constrained clustering (Chapter 2), multi-study multi-class concordant biomarker detection (Chapter 3), and cancer model selection (Chapter 4) in high-throughput omics data analysis.

In Chapter 2, we proposed Constrained Gaussian Mixture Model (CGMM) by extending the Gaussian mixture model (GMM) to solve empty or small cluster issue. We also generalized CGMM to sparse CGMM (SCGMM) using L1 penalty for gene selection. Extensive simulations and three real applications demonstrated the superior performance of our proposed method.

In Chapter 3, we proposed a two-step framework, Multi-Study Multi-Class Concordance (MSCC), t detect biomarkers in multi-class analysis across multiple studies from the aspect of information theory. We first detect biomarkers with partially shared concordant patterns across multiple studies and then identify the studies which contribute to such concordance. The simulation and four real-world data applications showed superiority over min-MCC, the only existing method for this problem so far.

In Chapter 4, we developed Congruence Analysis and Selection of Cancer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in pathway-specific and drug-relevant manner using transcriptomics data. CASCAM provides harmonization between tumor and cancer model omics data, interpretable machine learning for congruence quantification, mechanistic investigation, and pathway-based topological visualization to determine the most appropriate cancer model selection. The workflow is presented using invasive lobular breast carcinoma (ILC) subtype, credentialing highly relevant models, while questioning congruence of some frequently used models such as MDA-MB-134VI for ILC research. Our novel method is generalizable to any cancer subtype and will be impactful for furthering research in precision medicine.

Public health significance: The proposed clustering, biomarker and cancer model selection methods using omics data are crucial for disease mechanistic understanding that can lead to translational and clinical research. The related researches unravel knowledge towards precision medicine and benefit public health.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

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

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

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

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 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.

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.

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.

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

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

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.

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 techniques are the most favored to analyze numerical data. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

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

Measures of Frequency

Measures of Central Tendency

Measures of Dispersion or Variation

Measures of Position

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

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

Inferential statistics

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

Here are two significant areas of inferential statistics.

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.

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.

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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Research Method

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Significance of the Study 5 Definition of Terms 7 Review of Literatures 7 IV. Research Methodology 17 V. Limitation/Delimitation 21 VI Data Analysis: 21 VII Data Verification: 24 VIII Summary and Conclusions: 25

Recommendations 26

Appendices 27 Research Questionnaire 27

References 34

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CHAPTER 1—INTRODUCTION6Purpose of the StudyProblem and Significance Research Question/Hypotheses7 Definition of TermsLimitations DelimitationsAssumptions

CHAPTER 2—LITERATURE REVIEW8CHAPTER 3—METHODOLOGY .Model Research DesignInstrument Data Collection Data AnalysisVariablesLimitations Delimitations AssumptionsCHAPTER 4—RESULTS Data Screening Scale Development Analyses of Primary Hypotheses Analyses of Secondary Hypotheses CHAPTER 5—DISCUSSION Discussion of Findings Implications of the Limitations on Present and Future Research Recommendations Practical Application of Results Future Research

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Research Methods
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis. Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative Data Analysis Methods for Your Dissertation
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.
Step 7: Data analysis techniques for your dissertation
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Dissertations 4: Methodology: Methods
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 …
11 Tips For Writing a Dissertation Data Analysis
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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 ...
Research Methods for Dissertation
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.
How to Analyse Secondary Data for a Dissertation
The process of data analysis in secondary research. Secondary analysis (i.e., the use of existing data) is a systematic methodological approach that has some clear steps that need to be followed for the process to be effective. In simple terms there are three steps: Step One: Development of Research Questions. Step Two: Identification of dataset.
Data Analysis
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.
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0.8 How Can Our SPSS Data Analysis Service For Dissertation Assist You. 1 How to Use SPSS to Analyze Dissertation Data: A Step-by-Step Guide. 1.1 Step #1: Importing Your Data to SPSS. 1.2 Step #2: Cleaning Your Data. 1.3 Step #3: Running Descriptive Statistics. 1.4 Step #4: Running Inferential Statistics.
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What Is a Research Methodology?
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We are 95% confident that the population mean value of the duration 72 hours after the tape application in control group is between 75.70 and 120.30 seconds. The pairwise comparisons (mean differences) were calculated for each pair of the duration. The mean difference between Kinesio tape and sham tape was 1.267 seconds (insignificant, p=1.000).
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The methods and procedures you will use to collect, store, and process the data To collect high-quality data that is relevant to your purposes, follow these four steps. Table of contents Step 1: Define the aim of your research Step 2: Choose your data collection method Step 3: Plan your data collection procedures Step 4: Collect the data
Dissertation Defense: Jian Zou
Dissertation Defense: Jian Zou. "Clustering, Biomarker and Cancer Model Selection Using Omics Data" - Public Health/Biostatistics, Central dogma reforms biomedical science. Since then, biomedical researchers have focused mostly on the relationship between DNA, RNA, and protein. To quantify their sequence, structure, and abundance, numerous ...
Data Analysis in Research: Types & Methods
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