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  • Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

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

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

  • The  aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

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

Table of contents

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

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

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

  • Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
  • Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

  • Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
  • Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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

  • Experimental research is primarily a quantitative method.
  • Interviews , focus groups , and ethnographies are qualitative methods.
  • Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

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

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

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

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

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

  • You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
  • You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

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

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

Standardising procedures

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

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

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

Creating a data management plan

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

  • If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
  • If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
  • You can prevent loss of data by having an organisation system that is routinely backed up.

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

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

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

  • Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
  • Double-check manual data entry for errors.
  • If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
  • You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

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

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

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

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

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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How to collect data for your thesis

Thesis data collection tips

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.

Rhetorical analysis illustration

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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 >>

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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.

Further Reading

Examples of qualitative research in pharmacy practice.

  • Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
  • Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
  • Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

  • Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
  • Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
  • Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
  • Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
  • Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

  • Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

  • Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

  • Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

  • Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
  • Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

  • Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]
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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:

primary research methods

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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Research Methodology

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.

What data collection best suits your research?

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analysis

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.

You May Also Like

A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies.

Action research for my dissertation?, A brief overview of action research as a responsive, action-oriented, participative and reflective research technique.

Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.

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data collection research dissertation

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 .

Research-Methodology

Data Collection Methods

Data collection is a process of collecting information from all the relevant sources to find answers to the research problem, test the hypothesis (if you are following deductive approach ) and evaluate the outcomes. Data collection methods can be divided into two categories: secondary methods of data collection and primary methods of data collection.

Secondary Data Collection Methods

Secondary data is a type of data that has already been published in books, newspapers, magazines, journals, online portals etc.  There is an abundance of data available in these sources about your research area in business studies, almost regardless of the nature of the research area. Therefore, application of appropriate set of criteria to select secondary data to be used in the study plays an important role in terms of increasing the levels of research validity and reliability.

These criteria include, but not limited to date of publication, credential of the author, reliability of the source, quality of discussions, depth of analyses, the extent of contribution of the text to the development of the research area etc. Secondary data collection is discussed in greater depth in Literature Review chapter.

Secondary data collection methods offer a range of advantages such as saving time, effort and expenses. However they have a major disadvantage. Specifically, secondary research does not make contribution to the expansion of the literature by producing fresh (new) data.

Primary Data Collection Methods

Primary data is the type of data that has not been around before. Primary data is unique findings of your research. Primary data collection and analysis typically requires more time and effort to conduct compared to the secondary data research. Primary data collection methods can be divided into two groups: quantitative and qualitative.

Quantitative data collection methods are based on mathematical calculations in various formats. Methods of quantitative data collection and analysis include questionnaires with closed-ended questions, methods of correlation and regression, mean, mode and median and others.

Quantitative methods are cheaper to apply and they can be applied within shorter duration of time compared to qualitative methods. Moreover, due to a high level of standardisation of quantitative methods, it is easy to make comparisons of findings.

Qualitative research methods , on the contrary, do not involve numbers or mathematical calculations. Qualitative research is closely associated with words, sounds, feeling, emotions, colours and other elements that are non-quantifiable.

Qualitative studies aim to ensure greater level of depth of understanding and qualitative data collection methods include interviews, questionnaires with open-ended questions, focus groups, observation, game or role-playing, case studies etc.

Your choice between quantitative or qualitative methods of data collection depends on the area of your research and the nature of research aims and objectives.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline.

John Dudovskiy

Data Collection Methods

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Data Collection – Methods Types and Examples

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Data collection

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.

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Muhammad Hassan

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  • Open access
  • Published: 22 May 2024

Feasibility and acceptability of a cohort study baseline data collection of device-measured physical behaviors and cardiometabolic health in Saudi Arabia: expanding the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) in the Middle East

  • Abdulrahman I. Alaqil   ORCID: orcid.org/0000-0003-0458-2354 1 , 2 , 3 ,
  • Borja del Pozo Cruz   ORCID: orcid.org/0000-0002-9728-1317 2 , 4 , 5 ,
  • Shaima A. Alothman   ORCID: orcid.org/0000-0003-2739-0929 6 ,
  • Matthew N. Ahmadi   ORCID: orcid.org/0000-0002-3115-338X 7 , 8 ,
  • Paolo Caserotti 2 ,
  • Hazzaa M. Al-Hazzaa   ORCID: orcid.org/0000-0002-3099-0389 6 , 9 ,
  • Andreas Holtermann   ORCID: orcid.org/0000-0003-4825-5697 3 ,
  • Emmanuel Stamatakis 7 , 8 &
  • Nidhi Gupta 3  

BMC Public Health volume  24 , Article number:  1379 ( 2024 ) Cite this article

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Physical behaviors such physical activity, sedentary behavior, and sleep are associated with mortality, but there is a lack of epidemiological data and knowledge using device-measured physical behaviors.

To assess the feasibility of baseline data collection using the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) protocols in the specific context of Saudi Arabia. ProPASS is a recently developed global platform for collaborative research that aims to harmonize retrospective and prospective data on device-measured behaviors and health. Using ProPASS methods for collecting data to perform such studies in Saudi Arabia will provide standardized data from underrepresented countries.

This study explored the feasibility of baseline data collection in Saudi Arabia between November and December 2022 with a target recruitment of 50 participants aged ≥ 30 years. Established ProPASS methods were used to measure anthropometrics, measure blood pressure, collect blood samples, carry out physical function test, and measure health status and context of physical behaviors using questionnaires. The ActivPal™ device was used to assess physical behaviors and the participants were asked to attend two sessions at (LHRC). The feasibility of the current study was assessed by evaluating recruitment capability, acceptability, suitability of study procedures, and resources and abilities to manage and implement the study. Exit interviews were conducted with all participants.

A total of 75 participants expressed an interest in the study, out of whom 54 initially agreed to participate. Ultimately, 48 participants were recruited in the study (recruitment rate: 64%). The study completion rate was 87.5% of the recruited participants; 95% participants were satisfied with their participation in the study and 90% reported no negative feelings related to participating in the study. One participant reported experiencing moderate skin irritation related to placement of the accelerometer. Additionally, 96% of participants expressed their willingness to participate in the study again.

Based on successful methodology, data collection results, and participants’ acceptability, the ProPASS protocols are feasible to administer in Saudi Arabia. These findings are promising for establishing a prospective cohort in Saudi Arabia.

Peer Review reports

Global data from 2023 indicate that an estimated 27.5% of adults do not meet physical activity guidelines and have poor physical behaviors (e.g., physical activity, sedentary behavior, and sleep) that are linked with an increased risk of morbidity and mortality [ 1 , 2 , 3 , 4 ]. Sufficient physical activity and sensible sedentary times are associated with better health outcomes (e.g., cardiovascular health, mental health, and physical function) [ 1 , 2 ]. Despite this fact, 50–90% of Saudi Arabian adults perform low or insufficient daily physical activity; about 50% spend at least five hours per day sitting [ 5 ]. Furthermore, around 33% of the population experiences sleep durations of less than 7 h per night [ 6 ]. These trends could be a reason why non-communicable diseases account for 73% of mortality and cardiovascular diseases account for 37% of all deaths among Saudi Arabian adults [ 7 ]. However, there have been few studies in Middle Eastern countries, and the evidence that links between physical behaviors and health outcomes is under-represented in Saudi Arabia [ 1 ].

Furthermore, within Saudi Arabia, the few studies exploring this connection often rely on self-reported physical behaviors that often do not provide the most accurate picture [ 5 , 8 , 9 , 10 , 11 ]. This lack of data necessitates studies that incorporate measurements from devices that directly track these behaviors among Saudi Arabian adults, which aligns with recent guidance from the World Health Organization (WHO) on the necessity of incorporating device-measured physical behaviors into future studies to explore their relationships with various health aspects [ 1 , 12 ]. By employing such a method, we can gain more precise insights into the dose-response relationships between different physical behaviors and various health outcomes among Saudi Arabian adults.

The Prospective Physical Activity, Sitting, and Sleep Consortium (ProPASS) is an initiative that aims to explore how thigh-based accelerometry measurement of physical behaviors influences a wide range of health outcomes. This initiative operates on a global scale and aims to harmonize data from both retrospective and future studies [ 13 ]. To fulfill the aim, ProPASS is developing methods for collecting prospective data and processing, harmonizing, and pooling data from previous and future studies [ 14 ]. To date, the methods of the ProPASS consortium have been used to harmonize data from large-scale epidemiological studies, such as the 1970 British Birth Cohort, the Australian Longitudinal Study on Women’s Health [ 15 ], and Norway’s Trøndelag Health Study (HUNT) [ 16 , 17 ]. As such, this study seeks to determine if the ProPASS methodologies will be effective in the context of data collection within Saudi Arabia. This will be beneficial because it will help to standardize the measurement of physical behaviors, enhance harmonization across studies, and create more a representative and valid understanding of the associations between physical behaviors and health globally, including under-represented countries such as Saudi Arabia.

This paper describes the feasibility of baseline ProPASS data collection in Saudi Arabia with prospectively harmonized data with the main resource. This feasibility study of baseline data collection will serve as a framework for a future cohort study that will investigate the associations between device-measured physical behavior (e.g., physical activity, sedentary behavior, and sleep) and cardiometabolic health in Saudi adults.

The study was approved by the Institutional Review Board at Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia (IRB 22–0146), and was carried out in accordance with the principles of the Declaration of Helsinki.

Study design and procedures

Participants were informed about the study’s aims and asked to read and sign the consent form before any measurements were taken. After agreeing to participate, they were asked to attend two sessions at the Lifestyle and Health Research Center (LHRC) at the Health Sciences Research Center of Princess Nourah Bint Abdulrahman University. During the first visit, each participant’s anthropometric measurements (e.g., height, weight, waist circumference), blood pressure and heart rate, blood samples, and handgrip strength were measured. Next, the participants completed questionnaires on demographic information, dietary habits, self-rated health, self-reported smoking status, and the Global Physical Activity, Sedentary Behaviors, and Sleep behavior questionnaires. At the end of the first visit, the researcher attached the ActivPAL™ accelerometer device to their thigh which they were asked to wear for seven consecutive days. Participants were also provided with a diary to record their waking and sleeping hours [ 18 ]. On the 8th day of study, the participants were asked to attend the LHRC for session two where they returned the device and were interviewed (see Fig.  1 ).

figure 1

Demonstration and summary of the study procedure

Participants and eligibility

The study aimed to recruit a total of 50 Saudi adults aged ≥ 30 years, which is generally considered a common sample size for feasibility studies [ 19 , 20 ]. The eligibility criteria were: (1) Saudi nationals (2), resident in Riyadh, and (3) aged ≥ 30 years old. The exclusion criteria were: (1) having a current medical condition that forces them to be chair-bound or bedridden for more than half of their waking hours (2), being allergic to plasters or adhesives (3), being allergic to low-density polyethylene (4), having a skin condition that would prevent them from wearing the monitor, and (5) those who may need to pass through a metal detector/security checkpoint during the duration of the study. The study’s aims, protocol, and procedures were clearly described to all participants before any measurements were taken.

Recruitment

Participant recruitment was carried out over the month of November 2022. Participants were recruited from different locations across Riyadh, Saudi Arabia, by using electronic flyers on social media (e.g., Twitter, WhatsApp) that provided information about the study and the researcher’s contact details. Prospective participants who were interested in joining the study were asked to provide their contact information via a link to Google Forms featured in the study description. The participants who initially expressed interest but later decided not to join were invited to share their reasons for non-participation through a physical or telephonic meeting.

Measurements based on ProPASS methodology

The current study employed the ProPASS method and protocol for new cohort studies that seek to join ProPASS prospectively [ 14 , 21 ]. All measurements were taken by researchers that were well-trained in the ProPASS protocol and methods. Blood pressure and hand grip strength measurements were taken three times, and the mean average was then calculated; all other measurements were taken only once.

Anthropometric measurements

Height (to the nearest 0.1 cm) and weight (to the nearest 0.1 kg) were measured with a stadiometer (SECA 284; Seca, Hamburg, Germany), and scale (SECA 284; Seca, Hamburg, Germany), respectively. Waist circumference (to the nearest 0.1 cm) was measured midway between the lower rib margin and the iliac crest at the end of a gentle expiration [ 22 ]. Body mass index (BMI) was calculated using the standard calculation (height in meters squared/body weight in kilograms).

Blood pressure and heart rate

Blood pressure was taken after resting for five minutes in a sitting position. Blood pressure was taken three times with one minute between measurements and the average reading was recorded [ 23 ]. Blood pressure and heart rate were measured using a Welch Allyn Connex 7300 Spot Vital Signs Monitor, which provides a high degree of accuracy [ 24 ]. Mean arterial pressure (MAP) was then calculated (MAP = 1/3 * SBP + 2/3 * DBP in mm Hg) using the average of both the SBP and DBP values [ 25 ].

Blood samples

Non-fasting finger-prick (capillary) blood samples (40 µL) were collected for analysis after warming the finger for five minutes. A drop of blood was taken directly from the heated finger to be analysed for blood glucose, triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. A previously validated CardioChek PA analyser (CardioChek PA Blood Analyser, UK) was used to analyse the blood samples [ 26 , 27 ].

Medication use

Participants’ medication use was evaluated by the question: Do you currently use any prescription medicines ? If the answer was yes, the participants were asked which medications they use, such as medication for high blood pressure, high cholesterol, asthma, COPD, anxiety, depression, thyroid problems, allergies. They were also asked whether the medication was in the form of tablets, or nasal sprays, whether the medication was anti-inflammatory, chemotherapeutic, urological, birth control, or neurological, and the age at which the participants had begun using the medication.

Familial disease history

Familial disease history was assessed by the question: Do your parents, siblings or children have, or have they ever had, some of the following diseases before the age of 60 ? The responses included asthma, hay fever/nasal allergies, chronic bronchitis, emphysema or COPD, anxiety or depression, myocardial infarction (heart attack), diabetes, stroke or brain hemorrhage, and cancer. The responses were yes, no , and I don’t know .

Chronic health status

Participants’ chronic disease status and/or long-term health issues were assessed by the question: Have you had, or do you have any of the following diseases? The responses included angina, myocardial infarction (heart attack), heart failure, peripheral vascular disease, atrial fibrillation, stroke/brain hemorrhage, thrombosis, pulmonary embolism, asthma, COPD or emphysema, diabetes, hypothyroidism (low metabolism), hyperthyroidism (high metabolism), cancer, migraine, psoriasis, kidney disease, arthritis (rheumatoid arthritis), Bechterew’s disease, gout, mental health problems, osteoporosis, sleep apnea, arthrosis, nerve disease, hearing/ear disease, eye disease, and infection. Those who replied yes were asked a follow-up question: How old were you when you had it for the first time?

Mobility limitations

The questionnaire was based on three questions on performance-based measures of mobility, which had already been translated and culturally adapted into Arabic [ 28 ]. These three questions are valid and reliable tools to identify the early indications of disability and can be used as indicators to identify those at high risk of future disability [ 29 ]. Self-reported mobility was assessed via the following questions: (1)  Do you have difficulty in walking 2.0 km?  (2)  Do you have difficulty in walking 0.5 km ? and (3)  Do you have difficulty in walking up one flight of stairs? The five response options were: (1)  able to manage without difficulty  (2), able to manage with some difficulty  (3), able to manage with a great deal of difficulty  (4), able to manage only with the help of another person, and  (5)  unable to manage even with help.

Dietary habits

The dietary habits questionnaire was translated and culturally adapted into Arabic [ 28 ]. The questionnaire assessed the dietary habits of the participants was adapted from the Survey of Health, Aging, and Retirement in Europe (SHARE), which has been demonstrated to be a valid and reliable tool for assessing diet [ 30 ]. The questionnaire focused on the consumption of dairy products, legumes, eggs, meat, fruit and vegetables.

Self-rated health

A set of valid and reliable questions adapted from Idler et al.’s (1997) questionnaire was used to assess participants’ self-rated health by asking them to rate their health status using the following questions: (1)  In general, would you say your health is…: Excellent; Very good; Good; Fair; Poor;  (2)  Compared to one year ago, how would you rate your health in general now?: Much better now than one year ago; Somewhat better now than one year ago; About the same; Somewhat worse now than one year ago; Much worse now than one year ago [ 31 , 32 ].

Smoking habits

Self-report questions on smoking behavior were adapted from the UK Biobank questionnaire and were used to assess participants’ present and past smoking habits including at what age they began smoking. the number of cigarettes smoked per day, the type of tobacco used, the duration of smoking, and, among former smokers, the age when smoking ceased [ 33 ].

Physical behaviours

Physical behaviors such as physical activity, sedentary behavior, and sleep were measured by using (1) self-reported and (2) device-based measures:

Self-report measures

Physical activity was measured on a self-report basis via the Global Physical Activity Questionnaire (GPAQ) which was translated into Arabic and previously validated [ 34 ]. In addition, the Sedentary Behavior Questionnaire (SBQ), which had already been translated into Arabic [ 28 ], was used to subjectively assess participants’ sedentary behavior time [ 35 ]. Lastly, the Pittsburgh Sleep Quality Index was used to assess sleep quality and sleep disturbances over a one-month period [ 36 ].

Device-based measures

Physical behaviors were measured by wearing a thigh-worn accelerometer device (an ActivPAL™ Micro4, PAL technologies, Glasgow, Scotland) that participants wore continuously for 24 h for seven full days [ 37 ]. The Activpal™ device was sealed with a nitrile sleeve and attached with a medical waterproof 3 M Tegaderm transparent dressing on the front of the right mid-thigh on the muscle belly by a well-trained member of researcher team. The ActivPAL™ monitor is a valid and reliable measure of time spent walking [ 38 ], sitting, and standing time in healthy adults [ 39 ]. In addition, the participants were asked to fill in a recording sheet that included a sleep diary (times that the participant went to and got out of bed), as well as, the dates and times when the accelerometer fell off or was removed.

Physical function

Physical function was objectively measured using a digital hand-grip strength dynamometer (Takei Hand Grip Dynamometer 5401-C, Japan) via three successive hand-grip assessments for each hand (left and right); the mean value for each hand was then recorded. The instrument can measure hand-grip values from 5 to 100 kg; the minimum unit of measurement is 0.1 kg. The tool is a good health outcomes predictor [ 40 , 41 ].

Data collection evaluation of feasibility

Overall, the study evaluated feasibility in two main stages where feedback from the first six participants was used to resolve any unforeseen issues in the protocol implementation on the remaining participants. Any changes to the procedure were documented.

The current study evaluated the feasibility of Saudi adults’ participation based on the following constructs: (1) recruitment capability (2), acceptability and suitability of study procedures, and (3) resources and ability to manage and implement the study. Table  1 outlines the feasibility constructs, measures, outcome definitions, and methods employed. In evaluating feasibility, the current study followed the recommendations for a feasibility study as reported by Orsmond and Cohn, 2015 [ 42 ].

Overall, the study collected data on the feasibility constructs via tracking the registration, equipment availability, and time spent on various tasks performed (for example training researchers, performing various tasks like attaching the sensor) and completion rate (such as tracking diary entries, questionnaire entries and number of days with accelerometer data), via personal contacts (for information on barriers and facilitators of participation), via processing sensor data, and via interviews after the measurement (for example obtaining information on potential issues during measurement and willingness to participate).

Participant interviews after measurement

After the completion of the study, face-to-face semi-structured interviews were conducted with all participants who had completed the 7-day study period. The aim of these interviews was to collect comprehensive feedback regarding participants’ experiences with the study protocol, with the goal of capturing additional insights that was not captured by other feasibility measures. Some examples of such measures were motivations for joining the study, their expectations prior to participation, and their levels of satisfaction with the study procedures. A detailed interview guide is described in Appendix A [ 28 , 43 , 44 ].

Statistical analysis

Descriptive analysis summarized participants’ demographics, anthropometric measurements, health status, clinical measurements, physical behaviors characteristics, and interview questions responses. The continuous variables were characterized using mean ± standard deviations (SD), while categorical variables were presented using frequencies accompanied by percentages (%). The recruitment rate was calculated by the number of participants who participated and signed the consent form / total number of participants who registered in the study (see Fig.  2 ). Additional analyses were performed to compare participants who reported burden with those who reported no burden of participation (see supplementary materials). T-tests and Chi-square tests were employed for this comparison. IBM’s Statistical Package for the Social Sciences (SPSS) (version 27 SPSS, Inc. Chicago, Illinois) was used to conduct the qualitative analysis. The raw data of ActivPAL were analyzed by using the ActiPASS software (ActiPASS © 2021 - Uppsala University, Sweden).

figure 2

Recruitment and study participant’s diagram

A total of 75 participants initially volunteered to participate. Ten participants were excluded from the study as they did not meet the inclusion criteria ( n  = 8) or could not be contacted ( n  = 2). In addition, 11 participants withdrew their interest in participating for various reasons: (1) excessive distance between the location of the study (LRHC) and their residence ( n  = 3) (2), hesitant about joining the study ( n  = 1) (3), believed that the ActivPAL™ device would interfere with his/her health ( n  = 1) (4), believed that the ActivPAL™ device would interfere with their regular exercise routine ( n  = 2) (5), had family and work commitments ( n  = 3), and (6) claimed that the timing was unsuitable ( n  = 1). Out of a total of 54 participants who had agreed to participate in the study, 48 participants from Riyadh, Saudi Arabia, attended and completed the consent form. However, four of those participants provided incomplete data (i.e., they completed the questionnaires only and did not wear an ActivPAL™ device). Therefore, a total of 44 participants out of 75 potential participants (59%) successfully completed the study (wore an ActivPAL™ device and completed all questionnaires). See Fig.  2 for the study’s recruitment flow.

Participants

Of the 48 participants, nearly half were female (47.9%). On average, the participants were 37 ± 7.3 years old, had a BMI of 28.3 ± 5.6, and a waist circumference of 86.9 ± 16.4 cm. Most participants were married, had college degrees, were employed as office workers and professionals, had never smoked, and did not use any medication (see Table  2 ). A total of 87.5% of participants had a family history of disease; 85.4%, 95.8%, and 89.6%, reported having no difficulty walking 2 km, 500 m, and up one flight of stairs, respectively. Approximately 48% of participants rated their health as very good , while 39.6% reported their health as about the same compared to one year ago . In terms of dietary habits, nearly half the participants reported consuming dairy products every day, 25% consumed legumes and eggs 3 to 6 times a week, 56.3% consumed meat every day, and 45.8% consumed fruits and vegeTables 3, 4, 5 and 6 times a week.

Table  3 presents the primary variables of the study: including average systolic, diastolic, and mean arterial pressure values of 121.13 ± 11.81 mmHg, 79.26 ± 8.92 mmHg, and 93.15 ± 9.20 mmHg, respectively. The mean resting heart rate was 74.3 ± 12.66. Furthermore, the non-fasting blood profile of the sample was analyzed and showed the following values: total cholesterol: 177.89 ± 33.79 mg/dL; HDL-cholesterol: 50.96 ± 13.02 mg/dL; triglycerides: 123.94 ± 68.92 mg/dL; LDL-cholesterol: 103 ± 29.89 mg/dL; TC/HDL-cholesterol ratio: 3.71 ± 1.11; LDL/HDL-cholesterol ratio: 2.19 ± 0.81; non-HDL-cholesterol: 127.06 ± 33.51 mg/dL; non-fasting glucose: 102.98 ± 35.36 mg/dL. Table  3 provides an overview of the participants’ physical activity related behaviors.

Feasibility evaluation

The following results highlight the approaches taken by the current study to assess the feasibility of baseline data collection using ProPASS methodology specifically in the context of Saudi Arabia.

The evaluation of the feasibility of the study protocol was conducted in two stages, initially involving six participants, whose feedback was used to refine and improve the protocol implementation for the remaining participants. Of the six selected participants, three were female. In the pre-evaluation, only two minor issues were encountered; (1) accessing the lab outside of working hours (16:00–22:00) as most participants were unable to attend during the day (07:00–16:00) due to work commitments. This issue was resolved in all subsequent data collection points by receiving approval for extended lab hours; (2) obtaining the required number of ActivPAL™ devices from the technical coordinator due to miscommunication and high demand by other researchers. To prevent further issues, the author obtained 30 devices in advance for the feasibility evaluation.

Recruitment capability

The recruitment rate was used to measure the feasibility of recruitment methodology to collect baseline ProPASS data; the results showed that 64% ( n  = 48) of participants signed the consent form and attended the LRHC lab (see Fig.  2 ). After screening the eligibility criteria, out of a total of 75 participants, 65 met the study criteria, and 11 were excluded from participating due to the reasons as detailed in Fig.  2 . As Fig.  2 illustrates, although 54 participants scheduled an appointment for the study, only 48 (64%) attended and signed the consent form. In the final stage of the recruitment process, around 59% ( n  = 44) of participants completed all the required measurements for the study.

Acceptability and suitability of study procedures

The adherence rate (i.e., the extent to which participants adhered to the outlined procedures in terms of the number of days with valid accelerometry data) was 5.7 days. Furthermore, participants provided sleep diary entries for 85.4% of days. All questionnaires were completed with a 100% response rate.

To assess the study’s time demands on participants, the length of time participants needed to complete all measurements was mean time of 25 min (23 min to complete the questionnaires and two minutes to attach the sensor). Additionally, the completion rates for the registered participants who completed all the required measurements (i.e., accelerometer measurement, diary registration, and questionnaires) was 91.6%. (See Table  4 ).

Resources and ability

The final feasibility outcomes (i.e., having the required resources and ability to manage and implement the study) are presented in Table  5 . This objective was assessed based on four domains: skin irritation, equipment availability, training requirements, and accelerometer loss (see Table  5 ). The first domain revealed that three participants experienced skin irritation during the study; of these, two participants had mild symptoms, such as itchiness and discomfort that lasted for the first three days but did not lead to their withdrawal from the study. However, one participant reported moderate irritation resulting in red skin which required them to withdraw from the study. The second domain, equipment availability, indicated that all the necessary equipment was available 100% of the time. The third domain was training requirements, and the researchers required four hours of training on how to use it correctly. Finally, in the accelerometer loss domain, the study recorded four failed devices out of 30 that did not generate data for seven days.

Participant interview after measurement

After completing the study, all participants were interviewed around five primary themes: (1)  motivation and expectations of participation  (2), participant satisfaction  (3), the burden of participation  (4), willingness to participate again , and (5)  perception of time usage (see Fig.  3 ).

figure 3

Interview outcomes of participant’s experience with the study protocol

To determine the participants’ motivations for and expectations about joining the study, they were asked: What made you want to join this study? The results showed that 90% of participants were interested in learning about their physical behaviors and health status; 43% participated in supporting the researcher, and 14% reported that the final report attracted them to participate (see Fig.  3 a and the example of final report in supplementary material). Participant satisfaction was assessed via two questions: (1)  What was your overall experience of participating in the study? and (2)  Was it as you expected? The findings indicated that 62% of participants were satisfied that the study was as expected, 33% were more satisfied than expected, and 5% were unsatisfied and found the study below their expectations (see Fig.  3 b).

Regarding the overall burden of participation, 76% of participants reported that it was no burden , 5% reported that it was a burden , and 14% believed it was somewhat burdensome (see Fig.  3 c). Additionally, 79% of participants expressed their willingness to participate again in the future (see Fig.  3 d). Finally, regarding time usage, 67% of participants found it easy to complete the seven-day study without any concerns (see Fig.  3 h).

The feasibility of the baseline ProPASS data collection methodology was evaluated among Saudi adults who participated in this study. The findings revealed that the methodology was both feasible and acceptable, paving the way for large-scale prospective cohort research in Saudi Arabia. This research marks the first attempt to establish a prospective cohort study in Saudi Arabia using established ProPASS methods [ 13 , 15 ] and protocols. Conducting such a cohort study in Saudi Arabia is crucial due to the country’s high prevalence of non-communicable diseases that are mostly due to poor physical behaviors (e.g., lack of physical activity, sedentary behavior, and sleep) [ 7 ], due to recent enormous economic growth accompanied by technological transformations and urbanization [ 11 ].

The first aspect of feasibility evaluated of the baseline ProPASS data collection methodology was the capability to recruit participants. The findings indicated that the recruitment rate was 64% which is similar to prior studies [ 46 , 47 ]. One study indicated that a recruitment rate of at least between 20 and 40% is required to be deemed feasible [ 48 ]. Thus, the recruitment rate in the current study seems acceptable for creating a future cohort using ProPASS methods in Saudi Arabia. Additionally, in the current study, the refusal rate was only 15% which is significantly lower than in previous studies [ 45 , 49 ] where refusal rates ranged from 50 to 66%. One reason for the low refusal rate in the current study is that the recruitment was material specifically designed to motivate Saudi participants to join the study by indicating that the study would provide data and insight into their current state of health. For example, the results of the semi-structured interviews illustrated that 90% of participants joined the study because they wanted to know about their physical behaviors and health status (see Fig.  3 ). This result also indicates that our recruitment material might be suitable for ensuring high participation in the future cohort study.

The second aspect of feasibility for the baseline ProPASS data collection methodology that was evaluated in this study was the acceptability and suitability of the study procedures. Previous studies have shown that in order to obtain reliable estimates of adults’ habitual physical activity, it is necessary to record accelerometer data for 3–5 days [ 50 , 51 ] to gather valid data to perform analysis and provide information about the habitual physical behaviors. A recent study indicated that distributing accelerometers in person was associated with a high proposition of participants consenting to wear an accelerometer and meeting minimum wear criteria [ 21 ]. Our study was able to collect an average six days of valid data which was sufficient to obtain representative descriptions of the participants’ physical behaviors [ 52 ]. There were high general adherence rates for participant diary entries, questionnaires completion, and adherence to the study protocol, indicating that the ProPASS methods could be feasibly implemented with a larger study population. The study also assessed the time commitment necessary to complete the questionnaires and attach the ActivPAL™ devices to participants’ thighs. Completing the questionnaires took approximately 23 min (SD = 8). Prior studies have indicated that shorter questionnaires (e.g., 20 min) yield a higher response rate from participants, a finding that was consistent with our study [ 53 , 54 ]. Additionally, attaching the sensor to the participant’s thigh took about two minutes. These findings indicate that participation in this study was not burdensome, which was confirmed by the interviews that showed that 95% of participants felt that participating in the study (i.e., filling out all questionnaires and wearing the ActivPal™ device for 7 days) was not a burden. Overall, ProPASS methods appear to be less burdensome, well-suited, and readily accepted by participants.

The third aspect of feasibility for the baseline ProPASS data collection methodology was the availability of resources and the ability to manage and execute the study. As we aim to create a new cohort adhering to global (ProPASS) standards, protocol training was vital to obtain quality outcomes as per the ProPASS protocol. As a result, the protocol training took around four hours which was similar to a prior study [ 45 ]. In terms of the availability of resources, all essential equipment was always accessible. The study also considered skin irritation as an important factor. One study noted that 38% of participants stopped using ActivPal™ due to skin irritation from PALstickies or Tegaderm dressings [ 55 ]; another reported one discontinuation due to irritation associated with a Tegaderm dressing [ 56 ]. In the current study, there were three reported irritations, with two having mild initial discomfort that eventually subsided. One participant left the study due to moderate irritation. Nonetheless, it is important to note that the data collection occurred during colder winter periods (average 20 degrees Celsius). It is possible that instances of skin irritation could be more pronounced during Saudi Arabia’s hot summer season, characterized by temperatures of approximately 40 degrees Celsius. Future studies should investigate the feasibility of using devices and tape suitable for summer temperatures. In addition, the current study also had a low accelerometer failure rate: only four accelerometers failed to record, which is similar to previous studies [ 57 , 58 ]. All ActivPal™ devices were returned at the end of the study during visit two, ensuring that the ProPASS method is suitable to be used in future cohorts in Saudi Arabia.

Strengths and limitations of Study

This study represents the first of its kind to utilize device-based measures for assessing physical behaviors among adults in Saudi Arabia. The device-based measure has been shown to provide useful information about physical behaviors when compared to using self-report questionnaires [ 16 ]. Furthermore, it marks the initial examination of the ProPASS consortium method in the Middle East, particularly in Saudi Arabia. Nevertheless, the current study has certain limitations including recruiting among relatively young participants, presumably without any medical conditions and with postgraduate qualifications. This may limit the generalization of the findings to the entire population. The acceptability of the study in other age groups and among individuals with lower educational backgrounds is yet to be studied. In addition, the feasibility of the baseline ProPASS data collection methodology study was conducted during winter, which might have influenced the observed levels of physical behaviors in our sample. Similarly, the study was unable to evaluate the feasibility of utilizing 3 M Tegaderm dressings in hot summer months. Lastly, it’s important to note that our study employed a relatively small sample size; nonetheless, this size is considered acceptable for feasibility studies.

The baseline ProPASS data collection methodology and protocol for a future cohort study are both feasible and acceptable for implementation within the context of Saudi Arabia. This feasibility study represents the first step toward establishing a prospective ProPASS cohort study to examine the association between physical behaviors and cardiometabolic health among Saudi Arabian adults.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

The Prospective Physical Activity, Sitting and Sleep consortium

Physical activity, sedentary behavior, and sleep

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Acknowledgements

The authors would like to express gratitude to all participants for their involvement in the study. Additionally, we extend our appreciation to the research assistants (Rasil Alhadi, Ragad Alasiri, and Khalid Aldosari) who assisted in the data collection. Finally, we would like to thank the LHRC, Princess Nourah Bint Abdulrahman University for providing their site for collecting the data.

This research was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GrantA353]. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

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Conceptualization: AIA, NG, ES, and BdCMethodology: AIA, NG, ES, HMA, and BdCInvestigation: AIAData collection: AIAInterpretation of the findings: AIA, HMA, ES, NG, AH, PC, MNA, and BdCDrafting the paper: AIAReviewing and editing the draft: AIA, ES, HMA, BdC, SAA, PC, MNA, AH, and NGAll authors critically read, revised the draft for important intellectual content, approved the final version of the manuscript to be published, and agreed to be accountable for all aspects of the work.

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Alaqil, A.I., del Pozo Cruz, B., Alothman, S.A. et al. Feasibility and acceptability of a cohort study baseline data collection of device-measured physical behaviors and cardiometabolic health in Saudi Arabia: expanding the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) in the Middle East. BMC Public Health 24 , 1379 (2024). https://doi.org/10.1186/s12889-024-18867-2

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  • Feasibility
  • Epidemiology
  • Physical activity
  • Physical behavior
  • Sedentary behaviors
  • Accelerometry
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Enhancing environmental research: web scraping and sustainability.

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Climate change, pollution, environmental degradation and resource depletion are just a few of the greatest challenges facing the world in the 21st century. Tackling these challenges means carrying out thorough research involving a huge pool of world data from sources such as satellite imagery, environmental monitoring stations and local field studies, providing an array of information on human interaction with nature.

But the real challenge comes in capturing massive amounts of real-time data so wise decisions can be made within the shortest time possible. To do this, we must better understand the role of modern data collection technologies in environmental research and how we can adapt our strategies to leverage these technologies more effectively.

Introduction To Environmental Research

Historical records show that concern for the environment goes back thousands of years. In fact, in 2700 BC, some of the first known laws were implemented to protect trees from continued deforestation in Ur, Mesopotamia. Centuries later, the establishment of Scotland's Coal Smoke Abatement Society in 1898 marked a significant community-driven response to environmental issues.

These movements have been fueled in the 21st century by advancements in technology—particularly in computing and data analysis—that have significantly impacted the research industry as a whole, beyond just the environmental sector. This then begs the question: With large-scale, global environmental data collection, how do we integrate and put such information to use to address environmental challenges?

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Here’s what happens if trump is convicted—as trial goes to jury next week, biden vs trump 2024 election polls trump leads by 2 points latest survey shows, worldwide data collection for environmental insights.

Environmental monitoring is critical for evaluating vital factors such as air and water quality. Across the globe, various organizations and research groups deploy sophisticated data collection techniques to track environmental health.

For instance, the World Air Quality Index project collects data from over 12,000 stations in more than 1,000 cities worldwide. This project aggregates real-time data on air pollution levels, which is crucial for assessing health risks, informing the public and shaping policy decisions. Most of this data is publicly available, allowing analysts and researchers to utilize it for further studies and environmental assessments.

In the U.S., the National Weather Service gathers data from weather stations nationwide. This data, essential for generating accurate weather forecasts, plays a crucial role in emergency preparedness, particularly in areas susceptible to extreme weather events. This information is also generally accessible to the public, supporting a variety of applications from academic research to commercial use in weather-related industries.

Furthermore, conservation organizations like the World Wildlife Fund monitor deforestation, wildlife trafficking and illegal fishing activities. Public access to this data can vary, with some information available for open use to aid in raising awareness and promoting conservation efforts.

This transparency not only promotes accountability but also fosters a collaborative approach to tackling environmental challenges.

The Impact Of Web Scraping On Environmental Policy And Advocacy

In the context of environmental research and policymaking, the ability to access information without source limitations can be a significant advantage. Web scraping can be a useful tool in this scenario, enabling researchers, policymakers and advocates to gather and analyze data from virtually any online source, irrespective of geographical and linguistic boundaries. This capability is particularly helpful in environmental science, in which the global nature of challenges like climate change, pollution and biodiversity loss demands a comprehensive understanding that spans across nations.

Considerations When Using Web Scraping For Environmental Purposes

Ensure data integrity..

Credibility checking of sources is important to confirm the dependability of data collected using web scraping. If the data that web scraping provides is incorrect, it could lead to misguided decisions and policymaking; hence, data accuracy is the top-most priority. The data needs to be updated regularly to keep it relevant, and using as many sources as possible for cross-verification will add strength to your dataset.

Use data effectively.

Data can be used to track changes in the environment, monitor ecosystem health and inform policymakers. Actively participate in the collection and data analysis processes to ensure optimal environmental management.

Use high-level analytic tools.

Integrate advanced analytical tools and technologies to further enhance the value of the data collected through web scraping. Tools like AI and machine learning can help identify patterns and predictions that might not be evident through traditional analysis methods. Incorporating these technologies can provide deeper insights into environmental data, leading to more effective strategies and solutions.

Looking Ahead: The Evolving Role Of Data In Environmental Advocacy

Web scraping has emerged as one important tool in environmental research, offering a method to rapidly collect and analyze data from a multitude of sources. As technology advances, the capabilities of data collection technology will expand, offering even more sophisticated tools for data extraction and analysis. These advancements will not only enhance the accuracy and depth of environmental research but also open new avenues for public engagement and policy influence.

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Cars & Consumer Data: On Unlawful Collection & Use

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Some say the car a person drives can say a lot about them. As cars get “connected,” this turns out to be truer than many people might have realized. While connectivity can let drivers do things like play their favorite internet radio stations or unlock their car with an app, connected cars can also collect a lot of data about people. This data could be sensitive—such as biometric information or location—and its collection, use, and disclosure can threaten consumers’  privacy and financial welfare .

Connected cars have been on the FTC’s radar for years. The FTC highlighted concerns related to connected cars as part of an “Internet of Things”  workshop held in 2013, followed by a  2015 report . In 2018, the FTC hosted a  connected cars workshop highlighting issues ranging from unexpected secondary uses of data to security risks. The agency has also published  guidance to consumers reminding them to wipe the data on their cars before selling them—much as anyone would when trying to resell a computer or smart phone.

Over the years, privacy advocates have raised concerns about the vast amount of data that could be collected from cars, such as  biometric , telematic, geolocation, video, and other personal information. News reports have  also   suggested that data from connected cars could be used to stalk people or affect their insurance rates. Many have noted that when any company collects a large amount of sensitive data, it can pose national security issues if that data is shared with foreign actors.

Car manufacturers—and all businesses—should take note that the FTC will take action to protect consumers against the illegal collection, use, and disclosure of their personal data. Recent enforcement actions illustrate this point:

  • Geolocation data is sensitive and subject to enhanced protections under the FTC Act . Cars are much like mobile phones when it comes to revealing consumers’ persistent, precise location. In a series of seminal cases in recent years, the Commission has established that the collection, use, and disclosure of location can be an unfair practice. In X-Mode , the FTC alleged that the data could be used to track people’s visits to sensitive locations like medical or reproductive health clinics, places of worship, or domestic abuse shelters. Similarly, in  InMarket, the Commission alleged that the company’s internal use of sensitive data to group consumers into highly sensitive categories for advertising purposes was unlawful. The orders resolving these matters prohibit these companies from selling sensitive location information.
  • Surreptitious disclosure of sensitive information can be an unfair practice. Companies that have legitimate access to consumers’ sensitive information must ensure that the data is used only for the reasons they collected that information. For example, the Commission recently alleged that BetterHelp , which offers online counseling services—including those marketed to specific groups like Christians, teens, and the LGBTQ+ community—revealed consumers’ email addresses and health questionnaire information to third parties for advertising purposes. Similarly, the Commission  took action against mental telehealth provider Cerebral for, among other things, the company’s unfair privacy and security practices. The FTC obtained settlements requiring BetterHelp and Cerebral to pay millions of dollars so that affected consumers could receive partial refunds, and the Cerebral settlement bans the company from using or disclosing consumers’ personal information for advertising purposes.
  • Using sensitive data for automated decisions can also be unlawful.  Companies that feed consumer data into algorithms may be liable for harmful automated decisions. The FTC recently took action against Rite Aid, saying in a  complaint that the company enrolled people into a facial recognition program that alerted employees when suspected matches entered their stores. The complaint includes allegations that Rite Aid failed to take reasonable steps to prevent low-quality images from being used with the program, increasing the likelihood of false-positive match alerts. In some cases, false alerts came with recommended actions, such as removing people from the store or calling the police, and employees followed through on those recommendations. As a result of the FTC’s action, Rite Aid agreed to a 5-year ban on the use of facial recognition technology.

These cases underscore the significant potential liability associated with the collection, use, and disclosure of sensitive data, such as biometrics and location data. As the FTC  has stated , firms do not have the free license to monetize people’s information beyond purposes needed to provide their requested product or service, and firms shouldn’t let business model incentives outweigh the need for meaningful privacy safeguards.

The easiest way that companies can avoid harming consumers from the collection, use, and sharing of sensitive information is by simply not collecting it in the first place. When they are motivated to, all businesses—including auto manufacturers—are capable of building products with safeguards that protect consumers. 

Thank you to staff from across the Office of Technology and the Division of Privacy and Identity Protection in the Bureau of Consumer Protection who collaborated on this post.

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Princeton University

Can ‘forever’ chemicals become less so this senior thesis works toward smarter cleanup of pfas..

By Molly Sharlach

May 20, 2024

Student and professor having a discussion while student points at information in a notebook.

For her senior thesis, Amélie Lemay worked with Ian Bourg, an associate professor of civil and environmental engineering and the High Meadows Environmental Institute. She developed complex simulations of how PFAS molecules, a critical class of environmental contaminants, move and interact at the interface of water and air. Photos by Sameer A. Khan/Fotobuddy

The class of chemicals known as PFAS — used in firefighting foams, some nonstick cookware, and many other products — can resist heat and repel water. Their chemical bonds are hard to break, and they persist in water sources for decades.

Exposure to them has been associated with cancers, “impacts to the liver and heart, and immune and developmental damage to infants and children,” according to the Environmental Protection Agency, which recently set national limits for PFAS in drinking water.

For her thesis research, Princeton senior Amélie Lemay has crafted computer simulations that could one day help lead the way to removing PFAS pollution from the environment.

Lemay, a civil and environmental engineering major, used simulations to investigate how seven types of molecules behave above bodies of water, where the water meets the air. She modeled their tendencies to mix with water or stick to the water-air boundary, and probed how mixtures of PFAS molecules interact — mimicking the messy reality of contaminated water.

Detailed knowledge of this chemistry could be key to understanding how remediation methods will work in settings like water treatment plants. Over the next few years, utilities across the United States will need to find effective ways to remove PFAS (per- and polyfluoroalkyl substances) from drinking water to comply with the EPA limits.

“Most of our drinking water treatment plants are not set up to deal with these compounds,” said Lemay. “This type of research can eventually lead to better ways to be able to take PFAS out of water.”

Lemay, of Wynnewood, Pennsylvania, came to Princeton with aspirations of using engineering to address environmental challenges. But using computer simulations to understand pollution was not part of her plan.

The summer after her first year, in 2021, Lemay secured internship support from Princeton’s High Meadows Environmental Institute to conduct field work with associate professor Ian Bourg on how rocks weather in the Princeton area and in the French Alps — research with implications for soil nutrients and atmospheric CO2 forecasting.

But COVID-19 travel restrictions were still in place that summer, so Bourg worked remotely with Lemay and several other students to set up simulations exploring the behavior of pollutants ranging from PFAS to anti-inflammatory drugs to insecticides.

“I actually ended up really liking this alternative project, and I think it’s even better suited for me than the original project would have been,” said Lemay, who earned certificates in statistics and machine learning and sustainable energy .

Portrait of Amélie Lemay in an academic office with a laptop computer.

The research was an excellent opportunity for Lemay to build her computer coding skills and learn the intricacies of molecular dynamics simulation software.

“When I first started with Professor Bourg, he had to walk me through step by step how to create a file” simulating a single chemical compound, Lemay said. Over time, she learned to add more complexity, accounting for variables like salinity and surface tension. Now the work is “like second nature.”

The summer project was a new direction in the lab’s research. Bourg, an associate professor of civil and environmental engineering and the High Meadows Environmental Institute, said he was learning along with the students. He quickly realized that he could rely on Lemay: “She’s been thinking like a grad student since the very beginning, in terms of being super conscientious and questioning the way we do things,” said Bourg.

Lemay and Ethan Sontarp, a geosciences major, continued the project as research assistants in Bourg’s group for the next two years. Eventually, they modeled the behavior of more than 80 organic pollutants at the water-air interface.

Lemay and Sontarp were co-first authors of a 2023 paper reporting the results in the journal Environmental Science and Technology. The article has been downloaded more than 2,000 times and is Bourg’s most-read research paper from last year — a testament to its value as a resource for researchers looking to improve the tracking and remediation of pollutants, said Bourg.

In her junior year, Lemay conducted independent work with Professor Barry Rand , who studies the properties of new materials for solar cells, analyzing factors that influence the adoption of rooftop solar energy. She published this analysis last year in the journal Energy Policy.

For her senior thesis with Bourg, she developed complex simulations of how multiple PFAS molecules move and interact at the interface of water and air. Her results have revealed that the contaminants’ movements are not limited only by physical space but also by complex charge interactions among neighboring PFAS molecules.

Space-filling 3D models of two types of molecules; water molecules shown in red and white and PFAS molecules shown in pink and aqua.

Lemay is now submitting this work for scientific publication. The simulations are a powerful way to understand how pollutants move in the environment, potentially helping to explain how rain interacts with contaminants, and why sea spray and lake spray aerosols are an important source of PFAS exposure in coastal communities. Lemay hopes this understanding can inform strategies to clean up PFAS pollution.

Lemay turned to engineering in high school, when she took part in a summer research program on biomolecular engineering. “In science, you’re seeking to uncover the unknown, which is very important,” she said. “But I found that the problem-solving and design aspects of engineering really appealed to me. I loved how practical and pragmatic the applications were.”

After nearly three years of research at Princeton, Lemay has gained comfort with the uncertainties of the process. “If you pursue something, and you don’t fully understand what the data are showing you at first — that used to be distressing to me,” she said. “But I’ve come to realize that it’s part of the process. You’re trying to do something that’s never been done before. No one has the right answer.”

This summer, Lemay will pursue a project advised by Professor Mark Zondlo analyzing electric vehicle use and neighborhood-level air pollution.

In the fall, she will begin a Ph.D. program in civil and environmental engineering at the Massachusetts Institute of Technology. She’s interested in using computational methods to design chemicals for programmed degradation, to prevent problems with environmental contamination in the future.

“I think Princeton’s focus on undergraduate research really sets this institution apart,” said Lemay. “I’m grateful to have had the chance to work with multiple mentors who have shown me … how to design solutions and search for knowledge, and then share that with the greater community.”

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Energy and Environment

Related department.

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IDEES energy system database gets updated to improve analyses of policy assessments

A new version of a JRC-developed database simplifies data collection and data integration into modelling tools, allowing for an in-depth analysis of climate, energy and transport policy.

Image showing power grid

To better understand the current state of the energy system and support decision-making for climate, energy and transport policy, researchers and policy analysts require a wide range of data. 

For instance, they need to know how much energy different transport technologies consume in different Member States, and determine the potential for improvement in the future, based on the advancements made in these technologies over the past few decades. 

Facilitating this data collection and offering a solid starting point for researchers and analysts, the JRC has published an updated version of its open-access Integrated Database of the European Energy System (JRC-IDEES) , which consolidates a wealth of information, providing a granular breakdown of energy consumption and emissions. 

This comprehensive approach, which was also employed to support the European Commission's recommendations on climate action by 2040 , offers valuable insights into the dynamics shaping the European energy landscape, facilitating the assessment of past policies, technological advancements, structural shifts, and macroeconomic factors. 

Harmonised approach

First released in 2018, JRC-IDEES harmonises existing statistics with extensive technical assumptions to describe the recent history of all key sectors of the energy system: industry, the building sector, transport, and power generation. 

For each Member State, it breaks the energy use and emissions of each of these sectors down to the level of specific processes or technologies. This level of detail enables a granular analysis of recent changes in the energy system, for instance to assess past policies, technology dynamics, structural changes, and macro-economic factors. 

Since its initial release, JRC-IDEES has played an important role in EU research and policy analysis, serving as the primary data source for the JRC's Policy Oriented Tool for Energy & Climate Change Impact Assessment ( POTEnCIA model ).

New features

The latest update expands the time coverage of the database from 2000 to 2021 and incorporates new statistical sources as well as feedback from the user community. One key improvement is making the dataset easier to use within automated data workflows, so that researchers can better integrate JRC-IDEES into their analyses. 

The data is freely accessible under the Creative Commons BY 4.0 license, ensuring that it can be used by a wide range of stakeholders.

A technical report summarises the statistics and assumptions used to compile the database

Related links

Integrated Database of the European Energy System (JRC-IDEES) (dataset)

JRC-IDEES-2021: the Integrated Database of the European Energy System – Data update and technical documentation

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  1. Data Collection Methods

    Step 2: Choose your data collection method. Based on the data you want to collect, decide which method is best suited for your research. Experimental research is primarily a quantitative method. Interviews, focus groups, and ethnographies are qualitative methods. Surveys, observations, archival research, and secondary data collection can be ...

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    Collecting Data. 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.

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    research data. That is, they decide what methods of data collection (i.e., tests, questionnaires, interviews, focus groups, observations, constructed, secondary, and existing data) they will phys-ically use to obtain the research data. As you read this chapter, keep in mind the fundamental principle of mixed research originally defined in ...

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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 ...

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    For students and novice researchers, the choice of qualitative approach and subsequent alignment among problems, research questions, data collection, and data analysis can be particularly tricky. ... Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences. 2008. SAGE Research Methods.

  18. Data Collection Methods

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    the research setting, the sample, instrumen-tation (if relevant), and methods of data collection and analysis used. • Rationale and significance: Rationale is the justification for the study presented as a logical argument. Significance addresses the benefits that may be derived from doing the study, thereby reaffirming the research purpose.

  20. What Is a Research Design

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  25. Enhancing Environmental Research: Web Scraping And Sustainability

    For instance, the World Air Quality Index project collects data from over 12,000 stations in more than 1,000 cities worldwide. This project aggregates real-time data on air pollution levels, which ...

  26. Cars & Consumer Data: On Unlawful Collection & Use

    Car manufacturers—and all businesses—should take note that the FTC will take action to protect consumers against the illegal collection, use, and disclosure of their personal data. Recent enforcement actions illustrate this point: Geolocation data is sensitive and subject to enhanced protections under the FTC Act. Cars are much like mobile ...

  27. Can 'forever' chemicals become less so? This senior thesis works toward

    For her thesis research, Princeton senior Amélie Lemay has crafted computer simulations that could one day help lead the way to removing PFAS pollution from the environment. Lemay, a civil and environmental engineering major, used simulations to investigate how seven types of molecules behave above bodies of water, where the water meets the air.

  28. PDF Master's thesis research projects using tax data

    3. Common Report Standards data 4. Value Added Tax (VAT) data 5. Personal Income Tax (PIT) data 6. Corporate Income Tax (CIT) data For more information on the use of administrative data: • Please see this paper for an introduction to the tax data firm-level panel. • Please see this paper for a further guide on the tax data firm-level panel.

  29. IDEES energy system database gets updated to improve analyses of policy

    Facilitating this data collection and offering a solid starting point for researchers and analysts, the JRC has published an updated version of its open-access Integrated Database of the European Energy System (JRC-IDEES), which consolidates a wealth of information, providing a granular breakdown of energy consumption and emissions.