data collection in dissertation

Reference management. Clean and simple.

How to collect data for your thesis

Collecting theoretical data

Search for theses on your topic, use content-sharing platforms, collecting empirical data, qualitative vs. quantitative data, frequently asked questions about gathering data for your thesis, related articles.

After choosing a topic for your thesis , you’ll need to start gathering data. In this article, we focus on how to effectively collect theoretical and empirical data.

Empirical data : unique research that may be quantitative, qualitative, or mixed.

Theoretical data : secondary, scholarly sources like books and journal articles that provide theoretical context for your research.

Thesis : the culminating, multi-chapter project for a bachelor’s, master’s, or doctoral degree.

Qualitative data : info that cannot be measured, like observations and interviews .

Quantitative data : info that can be measured and written with numbers.

At this point in your academic life, you are already acquainted with the ways of finding potential references. Some obvious sources of theoretical material are:

edited volumes
conference proceedings
online databases like Google Scholar , ERIC , or Scopus

You can also take a look at the top list of academic search engines .

Looking at other theses on your topic can help you see what approaches have been taken and what aspects other writers have focused on. Pay close attention to the list of references and follow the bread-crumbs back to the original theories and specialized authors.

Another method for gathering theoretical data is to read through content-sharing platforms. Many people share their papers and writings on these sites. You can either hunt sources, get some inspiration for your own work or even learn new angles of your topic.

Some popular content sharing sites are:

With these sites, you have to check the credibility of the sources. You can usually rely on the content, but we recommend double-checking just to be sure. Take a look at our guide on what are credible sources?

The more you know, the better. The guide, " How to undertake a literature search and review for dissertations and final year projects ," will give you all the tools needed for finding literature .

In order to successfully collect empirical data, you have to choose first what type of data you want as an outcome. There are essentially two options, qualitative or quantitative data. Many people mistake one term with the other, so it’s important to understand the differences between qualitative and quantitative research .

Boiled down, qualitative data means words and quantitative means numbers. Both types are considered primary sources . Whichever one adapts best to your research will define the type of methodology to carry out, so choose wisely.

In the end, having in mind what type of outcome you intend and how much time you count on will lead you to choose the best type of empirical data for your research. For a detailed description of each methodology type mentioned above, read more about collecting data .

Once you gather enough theoretical and empirical data, you will need to start writing. But before the actual writing part, you have to structure your thesis to avoid getting lost in the sea of information. Take a look at our guide on how to structure your thesis for some tips and tricks.

The key to knowing what type of data you should collect for your thesis is knowing in advance the type of outcome you intend to have, and the amount of time you count with.

Some obvious sources of theoretical material are journals, libraries and online databases like Google Scholar , ERIC or Scopus , or take a look at the top list of academic search engines . You can also search for theses on your topic or read content sharing platforms, like Medium , Issuu , or Slideshare .

To gather empirical data, you have to choose first what type of data you want. There are two options, qualitative or quantitative data. You can gather data through observations, interviews, focus groups, or with surveys, tests, and existing databases.

Qualitative data means words, information that cannot be measured. It may involve multimedia material or non-textual data. This type of data claims to be detailed, nuanced and contextual.

Quantitative data means numbers, information that can be measured and written with numbers. This type of data claims to be credible, scientific and exact.

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Can J Hosp Pharm
v.68(3); May-Jun 2015

Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

What was treatment like 30 years ago?
Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
endless section threes.
Really…
But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
having dreadful side effects the next day I woke up [pause]
. . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
Oh how awful.
And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
[pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

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GETTING STARTED
Introduction
FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

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What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

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

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

It should include:

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

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

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

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

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

Option 1: Start with your “what”

What research problem or question did you investigate?

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

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

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

Option 2: Start with your “why”

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

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

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

Quantitative methods

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

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

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

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

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

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

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

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

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

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

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

Qualitative methods

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

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

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

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

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

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

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

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

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

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

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

Mixed methods

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

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

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

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

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

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

Specific methods might include:

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

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

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

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

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

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

1. Focus on your objectives and research questions

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

2. Cite relevant sources

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

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

3. Write for your audience

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

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

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

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

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

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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

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

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Methods of Data Collection – Guide with Tips

Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023

A key aspect of the dissertation writing process is to choose a method of data collection that would be recognised as independent and reliable in your field of study.

A well-rounded data collection method helps you communicate to the readers exactly how you would go about testing the research hypothesis or addressing the research questions – usually set out in the dissertation introduction chapter .

So what are the different methods of data collection you can use in your dissertation?

When choosing a dissertation method of data collection, there are certain elements you would need to keep in mind including the chosen topic, the established research aim and objectives, formulated research questions , and time and monetary limitations.

With several data collection methods to choose from, students often get confused about the most appropriate for their own research.

Here is a complete guide on the two research designs you can choose from in your dissertation – primary research and secondary research . The different research approaches within each of these two categories are explained below in detail.

Primary Research Strategy

Primary research involves data collection directly from participants. This data collection method is often chosen when the research is based on a certain area, a specific organisation, or a country.

Because the dissertation requires specific results and information, the primary research strategy is chosen to gather the required information and formulate results according to the research questions. There are various methods for conducting primary research:

Interviews are face-to-face discussions conducted directly with the participant(s). The matters raised during interviews are audio/video recorded or manually written down for subsequent analysis.

Participants are asked to fill out and sign a consent form before conducting the interviews. All questions asked during the interview are related to the research only.

Participants have the complete right to remain anonymous or reveal personal details if appropriate. Interviews are one of the most commonly used data collection strategies for dissertations employed by researchers.

Interviews are a flexible type of research. There are three types of interviews, depending on the extent to which they are structured – structured interviews , semi-structured interviews , and informal/unstructured interviews .

The researcher collects responses based on a set of established questions with little to no room for deviation from the pre-determined structure with structured interviews.
Unstructured interviews do not require the researcher to have a set of pre-agreed questions for the interview. The scope of this type of interview includes comprehensive areas of discussion. Responses are gathered by employing techniques such as probing and prompting.
Semi-structured interviews offer a balance between a formal interview’s focus and the flexibility of an unstructured interview.
In either case, the participant is informed beforehand of the nature of the interview they will be involved in.
While there is no strict rule concerning the number of participants an interview can involve, it would make sense to keep the group to 5-6 people. On the other hand, you can interview only one subject if that is more appropriate to your needs.

With the advent of technology, and to save time, many researchers now conduct online interviews and/or telephonic interviews. The timings and schedule are set before the day of the interview, and the participant is informed of the details via email. This helps in saving valuable time for the researcher, as well as the participant.

Not sure whether you should use primary or secondary research for your dissertation? Here is an article that provides all the information you need to decide whether you should choose primary or secondary research .

Surveys are another popular primary data collection method. The participants for this type of research design are chosen through a sampling method based on a selected population.

The researcher prepares a survey that consists of questions relating to the topic of research . These survey questions can be either open or close-ended .

Close-ended questions require the participant to choose from the multiple choices provided. If you are conducting a survey, you may decide not to meet the respondents due to financial or time constraints because surveys can be filled online or over a telephonic session.

On the other hand, open-ended questions do not have any options, and the respondent has the liberty to answer according to their own perception and understanding. For these types of surveys, meeting the participant in person would be the more fitting option.

Dissertations with close-ended questions are classified as quantitative research strategy dissertations. The data collected from these surveys are analysed through statistical tools such as SPSS or Excel.

Diverse tests are applied to the data depending on the research questions, aim, and objectives to reach a conclusion. For open-ended questions, qualitative analysis is conducted by thematic analysis and coding techniques.

Surveys are frequently conducted in market research, social sciences, and commercial settings.
Surveys can also be useful across a wide range of disciplines from business to anthropology.

Our writers have years of experience in dissertation research. Whether you need help with the full dissertation paper or just a part of it, ResearchProspect writers can help you achieve your desired grade.

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Questionnaires

Questionnaires are similar to closed-ended surveys. They contain standard questions and are distributed amongst a set of participants. A lot of researchers follow the Likert scale when using questionnaires.

This scale includes 5 options ranging from “strongly agree” to “strongly disagree”. The questionnaire consists of statements to which the respondents have to respond based on the specified options.

These responses are then analysed with the help of SPSS or another analytical tool by running analytical tests to create trend graphs and charts according to each statement’s responses.

Observation

This type of dissertation research design is usually used when the behaviour of a group of people or an individual is to be studied. The researcher observes the participants figure out how they behave in certain conditions.

There are two types of observations – overt and covert. Overt observation is usually adopted when observing individuals. Participants are aware that they are being observed, and they also sign a written consent form.

On the other hand, covert observation refers to observation without consent. The participant is not aware that researchers are studying them, and no formal consent forms are required to be signed.

Focus Groups

This dissertation data collection method involves collecting data from a small group of people, usually limited to 8-10. The whole idea of focus groups is to bring together experts on the topic that is being investigated.

The researcher must play the role of a moderator to stimulate discussion between the focus group members. However, a focus group data collection strategy is viral among businesses and organisations who want to learn more about a certain niche market to identify a new group of potential consumers.

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Secondary Research Strategy

Secondary research is the other research approach for dissertations, and it is usually chosen for its cost-effectiveness. Secondary research refers to the study and analysis of already published material on the subject.

This means that when a research topic is finalised, the research question is formulated and aims and objectives set up; the researcher starts to look for research and studies conducted in the past on the same topic. Reviewing and analysing those studies helps understand the topic more effectively and relate previous results and conclusions.

Researchers carried out secondary research when there was limited or no access to the participants relating to the thesis problem , even though there could be other reasons to choose a secondary data collection strategy, such as time constraints and the high cost of conducting primary research.

When using previous research, you should always be aware that it might have been carried out in a different setting with different aims and objectives. Thus, they cannot exactly match the outcome of your dissertation.

Basing your findings solely on one study will undermine the reliability of your work. Do your research, understand your topic and look for other researchers’ views in your field of study. This will give you an idea as to how the topic has been studied in the past.

Reviewing and analysing different perspectives on the same topic will help you improve your understanding, and you’ll be able to think critically about everything you read.

A thorough critical analysis will help you present the previous research and studies to add weight to your research work.

Results and discussion of secondary research are based on the findings mentioned in the previous studies and what you learned while reviewing and analysing them. There is absolutely nothing wrong if your findings are different from others who investigated the same topic.

The sources for this type of research include existing literature and research material (usually extracted from government bodies, libraries, books, journals, or credible websites).

If you are still unsure about the different research strategies you can use in your dissertation, you might want to get some help from our writers who will offer free advice regarding which method of research you should base your dissertation on.

Would you like some help with your dissertation methodology? We have academic experts for all academic subjects, who can assist you no matter how urgent or complex your needs may be.

Research prospect can help you with irrespective of the dissertation’s length; it can be partial or full. Please fill out our simple order form to place your order for the dissertation chapter – methodology . Or find out more about our dissertation writing services .

Frequently Asked Questions

What are the different methods of data collection.

Different methods of data collection include:

Surveys/questionnaires: Gather standardized responses.
Interviews: Obtain in-depth qualitative insights.
Observations: Study behaviour in natural settings.
Experiments: Manipulate variables to analyze outcomes.
Secondary sources: Utilize existing data or documents.
Case studies: Investigate a single subject deeply.

What is data collection?

Data collection is the systematic process of gathering and measuring information on variables of interest in an established systematic fashion, enabling one to answer relevant questions and evaluate outcomes. This process can be conducted through various methods such as surveys, observations, experiments, and digital analytics.

What methods of data collection are there?

Data collection methods include surveys, interviews, observations, experiments, case studies, focus groups, and document reviews. Additionally, digital methods encompass web analytics, social media monitoring, and data mining. The appropriate method depends on the research question, population studied, available resources, and desired data quality.

Which example illustrates the idea of collecting data?

A researcher distributes online questionnaires to study the impact of remote work on employee productivity. Respondents rate their efficiency, work-life balance, and job satisfaction. The collected data is then analysed to determine correlations and trends, providing insights into the effectiveness and challenges of remote work environments. This illustrates data collection.

What is qualitative data?

Qualitative data is non-numerical information that describes attributes, characteristics, or properties of an object or phenomenon. It provides insights into patterns, concepts, emotions, and contexts. Examples include interview transcripts, observational notes, and open-ended survey responses. This data type emphasises understanding depth, meaning, and complexity rather than quantification.

How to collect data?

Define the research question or objective.
Determine the data type (qualitative or quantitative).
Select an appropriate collection method (surveys, interviews, observations, experiments).
Design tools (e.g., questionnaires).
Conduct the data-gathering process.
Store and organise data securely.
Review and clean data for accuracy.

11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation.

Considering the complexity of many data analysis projects, it becomes challenging to get precise results if analysts are not familiar with data analysis tools and tests properly. The analysis is a time-taking process that starts with collecting valid and relevant data and ends with the demonstration of error-free results.

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Dissertation Data Analysis is the process of understanding, gathering, compiling, and processing a large amount of data. Then identifying common patterns in responses and critically examining facts and figures to find the rationale behind those outcomes.

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

For a proper dissertation data analysis, the student should have a clear understanding and statistical knowledge. Through this knowledge and experience, a student can perform dissertation analysis on their own.

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

2. Relevance of Collected Data

If the data is irrelevant and not appropriate, you might get distracted from the point of focus. To show the reader that you can critically solve the problem, make sure that you write a theoretical proposition regarding the selection and analysis of data.

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

On the other hand, quantitative analysis refers to the analysis and interpretation of facts and figures – to build reasoning behind the advent of primary findings. An assessment of the main results and the literature review plays a pivotal role in qualitative and quantitative analysis.

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly. It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal.

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

Qualitative Data Analysis Methods

Following are the methods used to perform quantitative data analysis.

Deductive Method

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

Inductive Method

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

Quantitative data contains facts and figures obtained from scientific research and requires extensive statistical analysis. After collection and analysis, you will be able to conclude. Generic outcomes can be accepted beyond the sample by assuming that it is representative – one of the preliminary checkpoints to carry out in your analysis to a larger group. This method is also referred to as the “scientific method”, gaining its roots from natural sciences.

The Presentation of quantitative data depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for hard sciences might require numeric inputs and statistics. As for natural sciences , such comprehensive analysis is not required.

Quantitative Analysis Methods

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

Trend analysis: This corresponds to a statistical analysis approach to look at the trend of quantitative data collected over a considerable period.
Cross-tabulation: This method uses a tabula way to draw readings among data sets in research.
Conjoint analysis : Quantitative data analysis method that can collect and analyze advanced measures. These measures provide a thorough vision about purchasing decisions and the most importantly, marked parameters.
TURF analysis: This approach assesses the total market reach of a service or product or a mix of both.
Gap analysis: It utilizes the side-by-side matrix to portray quantitative data, which captures the difference between the actual and expected performance.
Text analysis: In this method, innovative tools enumerate open-ended data into easily understandable data.

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs.

Tables help in presenting both qualitative and quantitative data concisely. While presenting data, always keep your reader in mind. Anything clear to you may not be apparent to your reader. So, constantly rethink whether your data presentation method is understandable to someone less conversant with your research and findings. If the answer is “No”, you may need to rethink your Presentation.

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part.

The data you find hard to arrange within the text, include that in the appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation.

8. Thoroughness of Data

It is a common misconception that the data presented is self-explanatory. Most of the students provide the data and quotes and think that it is enough and explaining everything. It is not sufficient. Rather than just quoting everything, you should analyze and identify which data you will use to approve or disapprove your standpoints.

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale.

It also involves answering what you are trying to do with the data and how you have structured your findings. Once you have presented the results, the reader will be looking for interpretation. Hence, it is essential to deliver the understanding as soon as you have submitted your data.

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation.

In the finding part, you should tell the reader what they are looking for. There should be no suspense for the reader as it would divert their attention. State your findings clearly and concisely so that they can get the idea of what is more to come in your dissertation.

11. Connection with Literature Review

At the ending of your data analysis in the dissertation, make sure to compare your data with other published research. In this way, you can identify the points of differences and agreements. Check the consistency of your findings if they meet your expectations—lookup for bottleneck position. Analyze and discuss the reasons behind it. Identify the key themes, gaps, and the relation of your findings with the literature review. In short, you should link your data with your research question, and the questions should form a basis for literature.

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

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

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Wrapping Up

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

In this article, we thoroughly looked at the tips that prove valuable for writing a data analysis in a dissertation. Make sure to give this article a thorough read before you write data analysis in the dissertation leading to the successful future of your research.

Oxbridge Essays. Top 10 Tips for Writing a Dissertation Data Analysis.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

Dissertation Preparation

Creating a Research Plan

Collecting Data

Writing a Dissertation
Function of Structures
Detailed Structures
Developing an Argument
Finding Dissertations
Additional Sources
Citation Management

For most research projects the data collection phase feels like the most important part. However, you should avoid jumping straight into this phase until you have adequately defined your research problem and the extent and limitations of your research. If you are too hasty you risk collecting data that you will not be able to use.

Consider how you are going to store and retrieve your data. You should set up a system that allows you to:
record data accurately as you collect it;
retrieve data quickly and efficiently;
analyze and compare the data you collect; and
create appropriate outputs for your dissertation e.g. tables and graphs, if appropriate.

Pilot Studies

A pilot study involves preliminary data collection, using your planned methods, but with a very small sample. It aims to test out your approach and identify any details that need to be addressed before the main data collection goes ahead. For example, you could get a small group to fill in your questionnaire, perform a single experiment, or analyze a single novel or document.

When you complete your pilot study you should be cautious about reading too much into the results that you have generated (although these can sometimes be interesting). The real value of your pilot study is what it tells you about your method.
Was it easier or harder than you thought it was going to be?
Did it take longer than you thought it was going to?
Did participants, chemicals, and processes behave in the way you expected?
What impact did it have on you as a researcher?

Spend time reflecting on the implications that your pilot study might have for your research project, and make the necessary adjustment to your plan. Even if you do not have the time or opportunity to run a formal pilot study, you should try and reflect on your methods after you have started to generate some data.

Dealing with Problems

Once you start to generate data you may find that the research project is not developing as you had hoped. Do not be upset that you have encountered a problem. Research is, by its nature, unpredictable. Analyze the situation. Think about what the problem is and how it arose. Is it possible that going back a few steps may resolve it? Or is it something more fundamental? If so, estimate how significant the problem is to answering your research question, and try to calculate what it will take to resolve the situation. Changing the title is not normally the answer, although modification of some kind may be useful.

If a problem is intractable you should arrange to meet your supervisor as soon as possible. Give him or her a detailed analysis of the problem, and always value their recommendations. The chances are they have been through a similar experience and can give you valuable advice. Never try to ignore a problem, or hope that it will go away. Also don’t think that by seeking help you are failing as a researcher.

Finally, it is worth remembering that every problem you encounter, and successfully solve, is potentially useful information in writing up your research. So don’t be tempted to skirt around any problems you encountered when you come to write-up. Rather, flag up these problems and show your examiners how you overcame them.

Reporting the Research

As you conduct research, you are likely to realize that the topic that you have focused on is more complex than you realized when you first defined your research question. The research is still valid even though you are now aware of the greater size and complexity of the problem. A crucial skill of the researcher is to define clearly the boundaries of their research and to stick to them. You may need to refer to wider concerns; to a related field of literature; or to alternative methodology; but you must not be diverted into spending too much time investigating relevant, related, but distinctly separate fields.

Starting to write up your research can be intimidating, but it is essential that you ensure that you have enough time not only to write up your research but also to review it critically, then spend time editing and improving it. The following tips should help you to make the transition from research to writing:

In your research plan, you need to specify a time when you are going to stop researching and start writing. You should aim to stick to this plan unless you have a very clear reason why you need to continue your research longer.
Take a break from your project. When you return, look dispassionately at what you have already achieved and ask yourself the question: ‘Do I need to do more research?’
Speak to your supervisor about your progress. Ask them whether you still need to collect more data.

Remember that you can not achieve everything in your dissertation. A section where you discuss ‘Further Work’ at the end of your dissertation will show that you are thinking about the implications your work has for the academic community.

<< Previous: Creating a Research Plan
Next: Writing a Dissertation >>

Library Guides

Dissertations 4: methodology: methods.

Introduction & Philosophy
Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.

Definitions

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:

Secondary sources

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.

Primary sources

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123).

Primary data

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316).

Secondary data

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).

Comparison between primary and secondary data

Use

Virtually all research will use secondary sources, at least as background information.

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'.

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.

Ultimately, you should state in this section of the methodology:

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis.

If using primary data, why you employed certain strategies to collect them.

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature).

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages.

Quantitative research

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496).

Qualitative research 

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs. 

Mixed methods

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years. 

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138).

Ultimately, your methodology chapter should state:

Whether you used quantitative research, qualitative research or mixed methods.

Why you chose such methods (and refer to research method sources).

Why you rejected other methods.

How well the method served your research.

The problems or limitations you encountered.

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis.

Whatever methods you will use, you will need to consider:

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose?

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?)

ethical considerations (see also tab...)

safety considerations

validity

feasibility

recording

procedure of the research (see box procedural method...).

Check Stella Cottrell's book Dissertations and Project Reports: A Step by Step Guide for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.

Experiments

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations.

For more information on Scientific Method, click here .

Observations

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.

Questionnaires and surveys

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements.

Interviews

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142).

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods.

Focus groups

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views.

This video focuses on strategies for conducting research using focus groups.

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box.

Case study

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.

Content analysis

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:

Research Methods

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection.

Doing your dissertation during the COVID-19 pandemic

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts;

Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication.

Case Study Research

Research Ethics

Quantitative Content Analysis

Sequential Analysis

Qualitative Content Analysis

Thematic Analysis

Social Media Research

Mixed Method Research

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!).

Include specifics about participants, sample, materials, design and methods.

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.

Describe all materials used for the study, including equipment, written materials and testing instruments.

Identify the study's design and any variables or controls employed.

Write out the steps in the order that they were completed.

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected.

Specify statistical techniques applied to the data to reach your conclusions.

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design.

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly.

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research.

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources. The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015). Research Methods for Business Students. New York: Pearson Education.

Specht, D. (2019). The Media And Communications Study Skills Student Guide . London: University of Westminster Press.

<< Previous: Introduction & Philosophy
Next: Ethics >>
Last Updated: Sep 14, 2022 12:58 PM
URL: https://libguides.westminster.ac.uk/methodology-for-dissertations

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

Table of Contents

Data Collection

Definition:

Data collection is the process of gathering and collecting information from various sources to analyze and make informed decisions based on the data collected. This can involve various methods, such as surveys, interviews, experiments, and observation.

In order for data collection to be effective, it is important to have a clear understanding of what data is needed and what the purpose of the data collection is. This can involve identifying the population or sample being studied, determining the variables to be measured, and selecting appropriate methods for collecting and recording data.

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

Primary data collection is the process of gathering original and firsthand information directly from the source or target population. This type of data collection involves collecting data that has not been previously gathered, recorded, or published. Primary data can be collected through various methods such as surveys, interviews, observations, experiments, and focus groups. The data collected is usually specific to the research question or objective and can provide valuable insights that cannot be obtained from secondary data sources. Primary data collection is often used in market research, social research, and scientific research.

Secondary Data Collection

Secondary data collection is the process of gathering information from existing sources that have already been collected and analyzed by someone else, rather than conducting new research to collect primary data. Secondary data can be collected from various sources, such as published reports, books, journals, newspapers, websites, government publications, and other documents.

Qualitative Data Collection

Qualitative data collection is used to gather non-numerical data such as opinions, experiences, perceptions, and feelings, through techniques such as interviews, focus groups, observations, and document analysis. It seeks to understand the deeper meaning and context of a phenomenon or situation and is often used in social sciences, psychology, and humanities. Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences.

Quantitative Data Collection

Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods. This data is typically collected through surveys, experiments, and other structured data collection methods. Quantitative data collection seeks to quantify and measure variables, such as behaviors, attitudes, and opinions, in a systematic and objective way. This data is often used to test hypotheses, identify patterns, and establish correlations between variables. Quantitative data collection methods allow for precise measurement and generalization of findings to a larger population. It is commonly used in fields such as economics, psychology, and natural sciences.

Data Collection Methods

Data Collection Methods are as follows:

Surveys involve asking questions to a sample of individuals or organizations to collect data. Surveys can be conducted in person, over the phone, or online.

Interviews involve a one-on-one conversation between the interviewer and the respondent. Interviews can be structured or unstructured and can be conducted in person or over the phone.

Focus Groups

Focus groups are group discussions that are moderated by a facilitator. Focus groups are used to collect qualitative data on a specific topic.

Observation

Observation involves watching and recording the behavior of people, objects, or events in their natural setting. Observation can be done overtly or covertly, depending on the research question.

Experiments

Experiments involve manipulating one or more variables and observing the effect on another variable. Experiments are commonly used in scientific research.

Case Studies

Case studies involve in-depth analysis of a single individual, organization, or event. Case studies are used to gain detailed information about a specific phenomenon.

Secondary Data Analysis

Secondary data analysis involves using existing data that was collected for another purpose. Secondary data can come from various sources, such as government agencies, academic institutions, or private companies.

How to Collect Data

The following are some steps to consider when collecting data:

Define the objective : Before you start collecting data, you need to define the objective of the study. This will help you determine what data you need to collect and how to collect it.
Identify the data sources : Identify the sources of data that will help you achieve your objective. These sources can be primary sources, such as surveys, interviews, and observations, or secondary sources, such as books, articles, and databases.
Determine the data collection method : Once you have identified the data sources, you need to determine the data collection method. This could be through online surveys, phone interviews, or face-to-face meetings.
Develop a data collection plan : Develop a plan that outlines the steps you will take to collect the data. This plan should include the timeline, the tools and equipment needed, and the personnel involved.
Test the data collection process: Before you start collecting data, test the data collection process to ensure that it is effective and efficient.
Collect the data: Collect the data according to the plan you developed in step 4. Make sure you record the data accurately and consistently.
Analyze the data: Once you have collected the data, analyze it to draw conclusions and make recommendations.
Report the findings: Report the findings of your data analysis to the relevant stakeholders. This could be in the form of a report, a presentation, or a publication.
Monitor and evaluate the data collection process: After the data collection process is complete, monitor and evaluate the process to identify areas for improvement in future data collection efforts.
Ensure data quality: Ensure that the collected data is of high quality and free from errors. This can be achieved by validating the data for accuracy, completeness, and consistency.
Maintain data security: Ensure that the collected data is secure and protected from unauthorized access or disclosure. This can be achieved by implementing data security protocols and using secure storage and transmission methods.
Follow ethical considerations: Follow ethical considerations when collecting data, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring that the research does not cause harm to participants.
Use appropriate data analysis methods : Use appropriate data analysis methods based on the type of data collected and the research objectives. This could include statistical analysis, qualitative analysis, or a combination of both.
Record and store data properly: Record and store the collected data properly, in a structured and organized format. This will make it easier to retrieve and use the data in future research or analysis.
Collaborate with other stakeholders : Collaborate with other stakeholders, such as colleagues, experts, or community members, to ensure that the data collected is relevant and useful for the intended purpose.

Applications of Data Collection

Data collection methods are widely used in different fields, including social sciences, healthcare, business, education, and more. Here are some examples of how data collection methods are used in different fields:

Social sciences : Social scientists often use surveys, questionnaires, and interviews to collect data from individuals or groups. They may also use observation to collect data on social behaviors and interactions. This data is often used to study topics such as human behavior, attitudes, and beliefs.
Healthcare : Data collection methods are used in healthcare to monitor patient health and track treatment outcomes. Electronic health records and medical charts are commonly used to collect data on patients’ medical history, diagnoses, and treatments. Researchers may also use clinical trials and surveys to collect data on the effectiveness of different treatments.
Business : Businesses use data collection methods to gather information on consumer behavior, market trends, and competitor activity. They may collect data through customer surveys, sales reports, and market research studies. This data is used to inform business decisions, develop marketing strategies, and improve products and services.
Education : In education, data collection methods are used to assess student performance and measure the effectiveness of teaching methods. Standardized tests, quizzes, and exams are commonly used to collect data on student learning outcomes. Teachers may also use classroom observation and student feedback to gather data on teaching effectiveness.
Agriculture : Farmers use data collection methods to monitor crop growth and health. Sensors and remote sensing technology can be used to collect data on soil moisture, temperature, and nutrient levels. This data is used to optimize crop yields and minimize waste.
Environmental sciences : Environmental scientists use data collection methods to monitor air and water quality, track climate patterns, and measure the impact of human activity on the environment. They may use sensors, satellite imagery, and laboratory analysis to collect data on environmental factors.
Transportation : Transportation companies use data collection methods to track vehicle performance, optimize routes, and improve safety. GPS systems, on-board sensors, and other tracking technologies are used to collect data on vehicle speed, fuel consumption, and driver behavior.

Examples of Data Collection

Examples of Data Collection are as follows:

Traffic Monitoring: Cities collect real-time data on traffic patterns and congestion through sensors on roads and cameras at intersections. This information can be used to optimize traffic flow and improve safety.
Social Media Monitoring : Companies can collect real-time data on social media platforms such as Twitter and Facebook to monitor their brand reputation, track customer sentiment, and respond to customer inquiries and complaints in real-time.
Weather Monitoring: Weather agencies collect real-time data on temperature, humidity, air pressure, and precipitation through weather stations and satellites. This information is used to provide accurate weather forecasts and warnings.
Stock Market Monitoring : Financial institutions collect real-time data on stock prices, trading volumes, and other market indicators to make informed investment decisions and respond to market fluctuations in real-time.
Health Monitoring : Medical devices such as wearable fitness trackers and smartwatches can collect real-time data on a person’s heart rate, blood pressure, and other vital signs. This information can be used to monitor health conditions and detect early warning signs of health issues.

Purpose of Data Collection

The purpose of data collection can vary depending on the context and goals of the study, but generally, it serves to:

Provide information: Data collection provides information about a particular phenomenon or behavior that can be used to better understand it.
Measure progress : Data collection can be used to measure the effectiveness of interventions or programs designed to address a particular issue or problem.
Support decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions.
Identify trends : Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
Monitor and evaluate : Data collection can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.

When to use Data Collection

Data collection is used when there is a need to gather information or data on a specific topic or phenomenon. It is typically used in research, evaluation, and monitoring and is important for making informed decisions and improving outcomes.

Data collection is particularly useful in the following scenarios:

Research : When conducting research, data collection is used to gather information on variables of interest to answer research questions and test hypotheses.
Evaluation : Data collection is used in program evaluation to assess the effectiveness of programs or interventions, and to identify areas for improvement.
Monitoring : Data collection is used in monitoring to track progress towards achieving goals or targets, and to identify any areas that require attention.
Decision-making: Data collection is used to provide decision-makers with information that can be used to inform policies, strategies, and actions.
Quality improvement : Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Characteristics of Data Collection

Data collection can be characterized by several important characteristics that help to ensure the quality and accuracy of the data gathered. These characteristics include:

Validity : Validity refers to the accuracy and relevance of the data collected in relation to the research question or objective.
Reliability : Reliability refers to the consistency and stability of the data collection process, ensuring that the results obtained are consistent over time and across different contexts.
Objectivity : Objectivity refers to the impartiality of the data collection process, ensuring that the data collected is not influenced by the biases or personal opinions of the data collector.
Precision : Precision refers to the degree of accuracy and detail in the data collected, ensuring that the data is specific and accurate enough to answer the research question or objective.
Timeliness : Timeliness refers to the efficiency and speed with which the data is collected, ensuring that the data is collected in a timely manner to meet the needs of the research or evaluation.
Ethical considerations : Ethical considerations refer to the ethical principles that must be followed when collecting data, such as ensuring confidentiality and obtaining informed consent from participants.

Advantages of Data Collection

There are several advantages of data collection that make it an important process in research, evaluation, and monitoring. These advantages include:

Better decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions, leading to better decision-making.
Improved understanding: Data collection helps to improve our understanding of a particular phenomenon or behavior by providing empirical evidence that can be analyzed and interpreted.
Evaluation of interventions: Data collection is essential in evaluating the effectiveness of interventions or programs designed to address a particular issue or problem.
Identifying trends and patterns: Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
Increased accountability: Data collection increases accountability by providing evidence that can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.
Validation of theories: Data collection can be used to test hypotheses and validate theories, leading to a better understanding of the phenomenon being studied.
Improved quality: Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Limitations of Data Collection

While data collection has several advantages, it also has some limitations that must be considered. These limitations include:

Bias : Data collection can be influenced by the biases and personal opinions of the data collector, which can lead to inaccurate or misleading results.
Sampling bias : Data collection may not be representative of the entire population, resulting in sampling bias and inaccurate results.
Cost : Data collection can be expensive and time-consuming, particularly for large-scale studies.
Limited scope: Data collection is limited to the variables being measured, which may not capture the entire picture or context of the phenomenon being studied.
Ethical considerations : Data collection must follow ethical principles to protect the rights and confidentiality of the participants, which can limit the type of data that can be collected.
Data quality issues: Data collection may result in data quality issues such as missing or incomplete data, measurement errors, and inconsistencies.
Limited generalizability : Data collection may not be generalizable to other contexts or populations, limiting the generalizability of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Dissertations and research projects

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What is a theoretical framework?

Developing a theoretical framework for your dissertation is one of the key elements of a qualitative research project. Through writing your literature review, you are likely to have identified either a problem that need ‘fixing’ or a gap that your research may begin to fill.

The theoretical framework is your toolbox . In the toolbox are your handy tools: a set of theories, concepts, ideas and hypotheses that you will use to build a solution to the research problem or gap you have identified.

The methodology is the instruction manual: the procedure and steps you have taken, using your chosen tools, to tackle the research problem.

Why do I need a theoretical framework?

Developing a theoretical framework shows that you have thought critically about the different ways to approach your topic, and that you have made a well-reasoned and evidenced decision about which approach will work best. theoretical frameworks are also necessary for solving complex problems or issues from the literature, showing that you have the skills to think creatively and improvise to answer your research questions. they also allow researchers to establish new theories and approaches, that future research may go on to develop., how do i create a theoretical framework for my dissertation.

First, select your tools. You are likely to need a variety of tools in qualitative research – different theories, models or concepts – to help you tackle different parts of your research question.

An overview of what to include in a theoretical framework: theories, models, ideologies, concepts, assumptions and perspectives.

When deciding what tools would be best for the job of answering your research questions or problem, explore what existing research in your area has used. You may find that there is a ‘standard toolbox’ for qualitative research in your field that you can borrow from or apply to your own research.

You will need to justify why your chosen tools are best for the job of answering your research questions, at what stage they are most relevant, and how they relate to each other. Some theories or models will neatly fit together and appear in the toolboxes of other researchers. However, you may wish to incorporate a model or idea that is not typical for your research area – the ‘odd one out’ in your toolbox. If this is the case, make sure you justify and account for why it is useful to you, and look for ways that it can be used in partnership with the other tools you are using.

You should also be honest about limitations, or where you need to improvise (for example, if the ‘right’ tool or approach doesn’t exist in your area).

This video from the Skills Centre includes an overview and example of how you might create a theoretical framework for your dissertation:

How do I choose the 'right' approach?

When designing your framework and choosing what to include, it can often be difficult to know if you’ve chosen the ‘right’ approach for your research questions. One way to check this is to look for consistency between your objectives, the literature in your framework, and your overall ethos for the research. This means ensuring that the literature you have used not only contributes to answering your research objectives, but that you also use theories and models that are true to your beliefs as a researcher.

Reflecting on your values and your overall ambition for the project can be a helpful step in making these decisions, as it can help you to fully connect your methodology and methods to your research aims.

Should I reflect on my position as a researcher?

If you feel your position as a researcher has influenced your choice of methods or procedure in any way, the methodology is a good place to reflect on this. Positionality acknowledges that no researcher is entirely objective: we are all, to some extent, influenced by prior learning, experiences, knowledge, and personal biases. This is particularly true in qualitative research or practice-based research, where the student is acting as a researcher in their own workplace, where they are otherwise considered a practitioner/professional. It's also important to reflect on your positionality if you belong to the same community as your participants where this is the grounds for their involvement in the research (ie. you are a mature student interviewing other mature learners about their experences in higher education).

The following questions can help you to reflect on your positionality and gauge whether this is an important section to include in your dissertation (for some people, this section isn’t necessary or relevant):

How might my personal history influence how I approach the topic?
How am I positioned in relation to this knowledge? Am I being influenced by prior learning or knowledge from outside of this course?
How does my gender/social class/ ethnicity/ culture influence my positioning in relation to this topic?
Do I share any attributes with my participants? Are we part of a s hared community? How might this have influenced our relationship and my role in interviews/observations?
Am I invested in the outcomes on a personal level? Who is this research for and who will feel the benefits?

One option for qualitative projects is to write an extended literature review. This type of project does not require you to collect any new data. Instead, you should focus on synthesising a broad range of literature to offer a new perspective on a research problem or question.

The main difference between an extended literature review and a dissertation where primary data is collected, is in the presentation of the methodology, results and discussion sections. This is because extended literature reviews do not actively involve participants or primary data collection, so there is no need to outline a procedure for data collection (the methodology) or to present and interpret ‘data’ (in the form of interview transcripts, numerical data, observations etc.) You will have much more freedom to decide which sections of the dissertation should be combined, and whether new chapters or sections should be added.

Here is an overview of a common structure for an extended literature review:

A structure for the extended literature review, showing the results divided into multiple themed chapters.

Introduction

Provide background information and context to set the ‘backdrop’ for your project.
Explain the value and relevance of your research in this context. Outline what do you hope to contribute with your dissertation.
Clarify a specific area of focus.
Introduce your research aims (or problem) and objectives.

Literature review

You will need to write a short, overview literature review to introduce the main theories, concepts and key research areas that you will explore in your dissertation. This set of texts – which may be theoretical, research-based, practice-based or policies – form your theoretical framework. In other words, by bringing these texts together in the literature review, you are creating a lens that you can then apply to more focused examples or scenarios in your discussion chapters.

Methodology

As you will not be collecting primary data, your methodology will be quite different from a typical dissertation. You will need to set out the process and procedure you used to find and narrow down your literature. This is also known as a search strategy.

Including your search strategy

A search strategy explains how you have narrowed down your literature to identify key studies and areas of focus. This often takes the form of a search strategy table, included as an appendix at the end of the dissertation. If included, this section takes the place of the traditional 'methodology' section.

If you choose to include a search strategy table, you should also give an overview of your reading process in the main body of the dissertation. Think of this as a chronology of the practical steps you took and your justification for doing so at each stage, such as:

Your key terms, alternatives and synonyms, and any terms that you chose to exclude.
Your choice and combination of databases;
Your inclusion/exclusion criteria, when they were applied and why. This includes filters such as language of publication, date, and country of origin;
You should also explain which terms you combined to form search phrases and your use of Boolean searching (AND, OR, NOT);
Your use of citation searching (selecting articles from the bibliography of a chosen journal article to further your search).
Your use of any search models, such as PICO and SPIDER to help shape your approach.
Search strategy template A simple template for recording your literature searching. This can be included as an appendix to show your search strategy.

The discussion section of an extended literature review is the most flexible in terms of structure. Think of this section as a series of short case studies or ‘windows’ on your research. In this section you will apply the theoretical framework you formed in the literature review – a combination of theories, models and ideas that explain your approach to the topic – to a series of different examples and scenarios. These are usually presented as separate discussion ‘chapters’ in the dissertation, in an order that you feel best fits your argument.

Think about an order for these discussion sections or chapters that helps to tell the story of your research. One common approach is to structure these sections by common themes or concepts that help to draw your sources together. You might also opt for a chronological structure if your dissertation aims to show change or development over time. Another option is to deliberately show where there is a lack of chronology or narrative across your case studies, by ordering them in a fragmentary order! You will be able to reflect upon the structure of these chapters elsewhere in the dissertation, explaining and defending your decision in the methodology and conclusion.

A summary of your key findings – what you have concluded from your research, and how far you have been able to successfully answer your research questions.

Recommendations – for improvements to your own study, for future research in the area, and for your field more widely.
Emphasise your contributions to knowledge and what you have achieved.

Alternative structure

Depending on your research aims, and whether you are working with a case-study type approach (where each section of the dissertation considers a different example or concept through the lens established in your literature review), you might opt for one of the following structures:

Splitting the literature review across different chapters:

This structure allows you to pull apart the traditional literature review, introducing it little by little with each of your themed chapters. This approach works well for dissertations that attempt to show change or difference over time, as the relevant literature for that section or period can be introduced gradually to the reader.

Whichever structure you opt for, remember to explain and justify your approach. A marker will be interested in why you decided on your chosen structure, what it allows you to achieve/brings to the project and what alternatives you considered and rejected in the planning process. Here are some example sentence starters:

In qualitative studies, your results are often presented alongside the discussion, as it is difficult to include this data in a meaningful way without explanation and interpretation. In the dsicussion section, aim to structure your work thematically, moving through the key concepts or ideas that have emerged from your qualitative data. Use extracts from your data collection - interviews, focus groups, observations - to illustrate where these themes are most prominent, and refer back to the sources from your literature review to help draw conclusions.

Here's an example of how your data could be presented in paragraph format in this section:

Example from 'Reporting and discussing your findings ', Monash University .

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Dissertations

Ennobling each other through collaborative inquiry: exploring music as a provocation for leadership development.

Ihan Ip , University of San Diego Follow

Date of Award

Degree name.

PhD Leadership Studies

Dissertation Committee

Cheryl Getz, EdD, Chair René Molenkamp, PhD, Member Donna Ladkin, PhD, Member

leadership development, arts-based methods, music, aesthetic knowing, facilitation, holding environment, safety, group process, collaboration, collective leadership, dialectics, paradigm shift, identity, inclusivity, belonging

Amid the challenges in a global village, leadership education needs to surpass traditional methods, nurturing creativity, flexibility, and adaptability. This study is a collaborative action inquiry that considers music as an arts-based method in service of leadership development. The study unfolded over five cycles, in which 14 coinquirers collaborated in a process of exploration. The study illuminates the strong potential of music as a provocation for leadership development and reveals crucial realizations in the area of facilitation in collective processes.

This dissertation tells the story of the inquiry with the voices of its coinquirers and offers insights on facilitation through my reflections as the researcher. It honored the fluidity of the process by gathering data through a combination of questionnaires, workshops, and individual conversations. Akin to the process of birthing or composition, both data collection and analysis occurred iteratively, accessing emergent, musically informed methods of sense-making.

This study found the intersection of music and leadership development emerged as a novel exploration. The findings underscore the importance of cocreating a holding environment, an awareness of the dynamics within collective leadership situations, scaffolding, and the profound value of experiential learning. Further, the findings reveal the necessity of strong mutual trust among group members. Music was shown to catalyze meaningful leadership education along the dimensions of identity, inclusivity, and belonging. The study also demonstrated music facilitates leadership learning at multiple levels—embodiment, awareness, and engagement in emergent processes—underscoring its potential as a transformative tool in leadership development.

Despite successfully designing musical leadership workshops, however, coinquirers experienced concern about taking their learning outside of the study. Implications for future research and practice include investigating the use of music in leadership in organizational contexts, finding ways to support leadership development facilitators to creatively use arts-based methods for their work, applying the use of music in social justice spaces, and developing more applied methods of leadership and facilitation through inquiry to deepen our ways of engaging with each other in our world.

Document Type

Dissertation: Open Access

Leadership Studies

Digital USD Citation

Ip, Ihan, "Ennobling Each Other Through Collaborative Inquiry: Exploring Music as a Provocation for Leadership Development" (2024). Dissertations . 1024. https://digital.sandiego.edu/dissertations/1024

Since May 13, 2024

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https://doi.org/10.22371/05.2024.015

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Comparison of Soil Carbon Dynamics Between Restored Prairie and Agricultural Soils in the U.S. Midwest

Globally, soils hold more carbon than both the atmosphere and aboveground terrestrial biosphere combined. Changes in land use and land cover have the potential to alter soil carbon cycling throughout the soil profile, from the surface to meters deep, yet most studies focus only on the near surface impact ( 3 and C 4 photosynthetic pathway plant community composition. Comparative analysis of edaphic properties and soil carbon suggests that deep loess deposits in Nebraska permit enhanced water infiltration and SOC deposition to depths of ~100 cm in 60 years of prairie restoration. In Illinois, poorly drained, clay/lime rich soils on glacial till and a younger restored prairie age (15 years) restricted the influence of prairie restoration to the upper 30 cm. Comparing the δ 13 C values of SOC and SIC in each system demonstrated that SIC at each site is likely of lithogenic origin. This work indicates that the magnitude of influence of restoration management is dependent on edaphic properties inherited from geological and geomorphological controls. Future work should quantify root structures and redox properties to better understand the influence of rooting depth on soil carbon concentrations. Fast-cycling C dynamics can be assessed using continuous, in-situ CO 2 and O 2 soil gas concentration changes. The secondary objective of my thesis was to determine if manual, low temporal resolution gas sampling and analysis are a low cost and effective means of measuring soil O 2 and CO 2 , by comparing it with data from in-situ continuous (hourly) sensors. Manual analysis of soil CO 2 and O 2 from field replicates of buried gas collection cups resulted in measurement differences from the continuous sensors. Measuring CO2 concentration with manual methods often resulted in higher concentrations than hourly, continuous measurements across all sites. Additionally, O 2 concentrations measured by manual methods were higher than hourly values in the restored prairie and less in agricultural sites. A variety of spatial variability, pressure perturbations, calibration offsets, and system leakage influences on both analysis methods could cause the discrepancy.

NSF Grant 1331906

Degree type.

Master of Science
Earth, Atmospheric and Planetary Sciences

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, additional committee member 5, usage metrics.

Environmental biogeochemistry
Soil chemistry and soil carbon sequestration (excl. carbon sequestration science)

COMMENTS

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"Ennobling Each Other Through Collaborative Inquiry: Exploring Music as
This dissertation tells the story of the inquiry with the voices of its coinquirers and offers insights on facilitation through my reflections as the researcher. ... and individual conversations. Akin to the process of birthing or composition, both data collection and analysis occurred iteratively, accessing emergent, musically informed methods ...
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Comparison of Soil Carbon Dynamics Between Restored Prairie and
The secondary objective of my thesis was to determine if manual, low temporal resolution gas sampling and analysis are a low cost and effective means of measuring soil O2 and CO2, by comparing it with data from in-situ continuous (hourly) sensors. ... Manual analysis of soil CO2 and O2 from field replicates of buried gas collection cups ...

How to collect data for your thesis

Collecting theoretical data

Qualitative Research: Data Collection, Analysis, and Management

THE PARTICIPANT’S VIEWPOINT

DATA COLLECTION

DATA ANALYSIS AND MANAGEMENT

Interpretation of Data

Transcribing and Checking

Reading between the Lines

DATA SYNTHESIS

Planning and Writing the Report

CONCLUSIONS

Appendix 1. Excerpt from a sample transcript

Further Reading

Qualitative Research in General

Group Dynamics in Focus Groups

Social Constructivism

Mixed Methods

Collecting Qualitative Data

Constructivist Grounded Theory

Data analysis techniques

Final thoughts...

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Methods of Data Collection – Guide with Tips

Primary Research Strategy

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