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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 Write a Data Collection Plan (Templates and Examples Included)

In a world where data drives decisions, how do you make sure you're gathering the right information? With a clear data collection plan in place, you ensure that the collected data leads to actionable insights.

Effective data collection is key to smart decision-making, grounding strategies in solid evidence rather than guesses. A well-designed data collection plan guarantees that you're collecting not just any data, but the right data, crucial for spotting trends, refining processes, and deeply understanding customer needs in any sector.

By the end of this article, you'll understand the importance of planning your data collection and how to do it effectively.

What is a data collection plan?

A data collection plan is a roadmap for identifying what data you need, the ways in which you'll collect it, and how you'll analyze it. The core purpose is to ensure that your data collection is targeted, efficient, and reliable, providing meaningful insights for your project or study.

Data collection plans should be developed at the start of a project or study, before any data is collected. Typically, this responsibility falls to project leaders, researchers, data analysts, or a designated team member with expertise in data management.

What does a typical data collection plan document cover

From setting clear objectives to establishing robust communication channels, each section of the plan is a stepping stone towards having a thorough data collection strategy:

• Objectives: Start with a specific goal for your data collection. Clearly state why this data is crucial and how it will impact your project or decision-making. This step ensures that every part of your plan aligns with your end goal.
• Data typology: Decide whether you need quantitative (numerical) or qualitative (descriptive) data. Clarify the importance of each data type in the context of your objectives. This clarity helps in selecting the right tools and methods for data collection.
• Collection methodology: Select appropriate methods like surveys, interviews, or analysis of existing data. Prioritize data quality; for surveys, this means clear, unbiased questions; for interviews, standardized interviewing techniques; etc.
• Data management protocols: Plan for the storage, organization, and protection of your data. Address ethical considerations, especially for sensitive information. Include a system for updating and correcting data to maintain its accuracy over time.
• Project timeline : Outline a realistic timeline with start and end dates, including key milestones. Incorporate flexibility for unforeseen delays or challenges.
• Needed resources: Identify the team, tools, and budget required. Clearly define roles and responsibilities to ensure a smooth data collection process.
• Data analysis strategy: Determine how you'll analyze the collected data. Include methods for dealing with unexpected findings, like ambiguous, conflicting, corrupted, or incomplete data.
• Feedback mechanisms:  Establish a mechanism for ongoing assessment and adjustment of your data collection methods. This allows you to adapt and refine your approach as needed.
• Communication framework: Decide how and when you'll communicate your findings. Depending on the project, you might need to keep stakeholders updated throughout the process, not just at the end, to maintain engagement and transparency.

Try to meticulously address each of these elements to set the stage for successful data gathering.

Ways to collect data

Collecting data is akin to gathering and sorting the pieces for a puzzle. Each piece, or data point, is critical to form a complete and accurate picture of the subject under study. 

To ensure that this picture is as clear and precise as possible, researchers and analysts employ a variety of data collection methods outlined in the image below.

• Surveys and questionnaires: These involve asking structured questions to a large group of people. Consider the timing of your survey distribution — sending out surveys at a time when your target audience is likely to be available and attentive can significantly improve the response quality.
• Interviews: One-on-one conversations that allow for deep dives into subjects' thoughts and experiences. Record interviews (with permission) and note non-verbal cues. These can provide context often lost in written notes, like the respondent's tone or hesitation.
• Focus groups: Small groups of people discuss specific topics, providing qualitative data on opinions and behaviors. Use a skilled moderator who can encourage quieter members to speak up and keep dominant personalities from overtaking the conversation.  
• Observations: Watching and recording behavior or events as they naturally occur. If possible, conduct observations at different times or in varied settings. This helps in understanding if the observed behavior is consistent or situation-dependent.
• Inspections and assessments: Examining objects, processes, or places in detail, often using a structured approach supported by pre-made checklists. 
• Document review and analysis: Systematically reviewing and interpreting existing documents to extract data. Cross-reference information from different documents for a more comprehensive understanding. This triangulation can validate findings and reveal deeper insights.

Each of these methods offers a unique way to gather data and comes with its own set of pros and cons. Take your time to decide which data collection methods are the best fit for your use case.

Steps for writing an effective data collection plan

With the theory out of the way, let’s see how to write a proper data collection plan, step by step.

1. Define objectives and research questions

Write down a statement of purpose that explains what you intend to discover, decide, or achieve. This statement will act as the compass for your data collection journey.

Your research questions must be clear, focused, and aligned with your stated objectives. For every objective, draft at least one research question that, when answered, will bring you closer to your goal. 

When finalizing your list of research questions, don't overlook the "so what?" factor. For each one, ask yourself what the implications are if the question is answered or the objective is met. How will it change your understanding, decision-making, or actions? This ensures that your plan has practical value and isn't just an academic exercise.

2. Identify data requirements and availability

Identifying your data requirements is a two-part process: you need to understand the type of data you need and assess the data that is already available to you. 

Here's how to understand the type of data you need:

• Consider the nature of your research questions: What data will provide the answers? Is it demographic information, behavioral metrics, financial statistics, etc.?
• Determine the data quantity: How much data is enough to make your results reliable? This can depend on the statistical methods you plan to use and the scale of your project.
• Think about the data quality: What level of accuracy is required? Does the data need to be current, historical, or predictive?

Create a data inventory list. For each research question, list the types of data that could potentially answer it. Next to each type, note down the attributes of the data you need (timeframe, demographic details, granularity, etc.).

To assess the data that is already available to you, follow these:

• Look internally first: Does your organization already have some of the data you need? This could be sales records, customer feedback, or past survey results.
• Consider external sources: Is there public data available that fits your needs, such as government databases, research papers, or industry reports?
• Evaluate accessibility: Can you easily access this data, or are there barriers (e.g., paywalls, privacy laws, data sharing agreements) that you need to consider?

For each piece of required data, try to record its source, format, any costs associated with obtaining it, and any potential challenges in accessing it. If data is not available, note down what proxies could be used or whether secondary data collection is necessary.

Completing this step will form the backbone of your data collection strategy, guiding you on where to focus your resources.

3. Choose how you will collect data

Based on your data requirements, select the most suitable collection methods. Will you use surveys, interviews, observations, experiments, or a combination of multiple methods? 

Match data collection methods to the type of data you need. For quantitative data, you might use surveys or sensor data. For qualitative data, consider interviews or focus groups. Think about the context of your research — does it call for controlled experiments, or would field studies yield better results?

Once you've selected a method, it's time to think about who will shoulder the task. The 'who' could range from your own team members to external professionals, depending on the expertise required.

Incorporate quality control measures right from the start. This should include when and where data will be collected, the tools or technologies used, and the step-by-step process for gathering the data.

Finally, address ethical considerations, especially if you’re dealing with human subjects or sensitive data. Obtain necessary permissions and ensure you’re compliant with relevant laws and regulations.

4. Outline how you will measure data and ensure its integrity

Clearly specify what you are measuring and how it will be quantified. Are you looking at frequencies, averages, percentages, or growth rates? Ensure that the chosen metrics align directly with your research questions and objectives.

Develop and document standardized procedures for data measurement: define operational terms, detail measurement techniques, and specify the equipment or software used.

For each variable, write down a clear operational definition, which is a detailed description of the procedures used to measure it . For example, if you're measuring customer satisfaction, define what constitutes satisfaction and the scale you're using (e.g., 1-5 likert scale ).

To ensure data integrity, team members tasked with collecting and analyzing data really need to know what they’re doing. If you’re using instruments or software, ensure they are calibrated and tested before data collection begins. Consider running a pilot study or trial to test your measurement processes and make adjustments where necessary. This helps you catch potential issues before you roll out large-scale data collection.

Create a data log that records when and by whom data was collected, entered, and verified. Make sure to regularly check a sample of data entries against the original data to ensure accuracy. If you’re using mobile forms or other digital tools to collect data, most of this can be automated. 

Lastly, decide in advance how you will deal with missing data or outliers. Will you use imputation methods , or will you exclude it? Make sure your approach is consistent and documented.

5. Decide how will data be analyzed and presented

Outlines each step of your analysis process: the methods you'll use, the required tools, and the sequence of analysis. 

Choose analysis methods that align with your data types and objectives. For analyzing quantitative data , statistical methods like regression analysis, ANOVA, or cluster analysis might be appropriate. For analyzing qualitative data , try content analysis, thematic analysis, or discourse analysis.

If you have a complex project and plan to use specific software to analyze data, decide which one that is going to be. Options could range from statistical software like SPSS or R for quantitative analysis to software like NVivo for qualitative data analysis.

Think about how you will present your data. This could be in the form of reports, infographics, dashboards, or presentations. Choose the format with your audience in mind — what format will be most clear and persuasive to them? 

Try sketching out a draft of your final report or presentation early in the planning process. This helps you visualize the end product and ensure that your data collection and analysis will support this outcome.

Data collection plan examples and templates

Below are four different examples and templates you can use to build your own data collection plans.

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Methods of Data Collection – Guide with Tips

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

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

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

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

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

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

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

Primary Research Strategy

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Questionnaires

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

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

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

Observation

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

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

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

Focus Groups

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

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

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Secondary Research Strategy

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

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

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

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

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

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

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

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

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

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

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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

Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.

Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement.

In correlational research, a researcher measures the relationship between two or more variables or sets of scores without having control over the variables.

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DMP for your PhD research

All first year post graduate researchers should complete a data management plan for their research and include it as part of their first three month review.  There is also a Blackboard course  Data Management Plans for Doctoral Students -  mandatory for all new doctoral students - to introduce you to research data management and help you complete the plan. Log into Blackboard using your university username and password.

A data management plan or DMP is a living document that helps you consider how you will organise your data, files, research notes and other supporting documentation throughout the length of the project.  The aim is to help you find these easily, keep them safe and have sufficient documentation to be able to re-use throughout your research and beyond.

You will need to complete a preliminary data management plan in your first three months, along with your Academic Needs Analysis.  Your DMP will continue to develop as your research progresses and you will need to update and review your DMP at every progression review. ( Code of Practice for Research Degree Candidature and Supervision, )

All researchers will have data. Data can be broadly defined as 'Material intended for analysis'.  This covers many forms and formats, and is not just about digital data.

For example, 

Art History - high resolution reproductions of photographs, notebook describing context

English literature - research notes on text, textual analysis

Engineering - experimental measurements on the physical properties of liquid metals

The University also has a definition for “Research Data” in its  Research Data Management Policy  that you should consider.

A PhD DMP template and guidance on how to complete your Data Management Plan is available ( see below ). All new doctoral students should complete the Data Management Plans for Doctoral Students module on Blackboard. Contact us if you need further information or have feedback via [email protected]

Guidance on depositing your research data at the end of your doctorate can be found on the Thesis Data Deposit guide. Please also see our depositing research data videos at  https://library.soton.ac.uk/researchdata/datasetvideos

Creating your DMP

• Introduction
• DMP and Project Overview
• About your Project Data
• Making Data Findable
• Making Data Accessible
• Making Data Reusable
• Making Data Secure
• Implementing the Plan
• Example Plans

What are data management plans? A data management plan is a document that describes:

• What data will be created
• What policies will apply to the data  
• Who will own and have access to the data
• What data management practices will be used 
• What facilities and equipment will be required 
• Who will be responsible for each of these activities

Your data management plan should be written specifically for the research that you will be doing.  Our template is a guide to help you identify the key areas that you need to consider, but not all sections will apply to everyone.  You may need to seek further guidance from your supervisor, colleagues in your department or other sources on best practice in your discipline.  We provide some details of guidance available in our training section and on our general research data management pages.

Each of the tabs looks at the different topics that can be included in a data management plan.  You can move through the tabs in any order.

Describing your Project

At the start of your data management plan (DMP) it is useful to include some basic information about the research you are planning to do.  This may already exist in other documents in more detail, but for the purposes of the DMP try to summarise in as few sentences as possible.

What policies will apply?

It is important that you think about who is funding your research and whether there are any requirements that you need to meet.  Are you funded by a UK Research Council? What policies do they have on research data - see  Funder Guidance .  What does our University Research Data Management policy  and Code for Conduct for Research state is required?

Does the type of data you will be creating, using, collecting mean that you have to meet certain legal conditions?  Will you be collecting any form of personal data, (see ICO Personal Data Definition ), special category data (see ICS Special Category definition ) or is it commercially sensitive?  For example, if you are involved in population health and clinical studies research data and records minimum retention could be 20-25 years for certain types of data - see the MRC Retention framework for research data and records  for further details. 

Do you need Ethics Approval?

Anyone who is dealing with human subjects or cultural heritage (see University policies ) will require to obtain ethics approval and this must be done prior to collecting any data.  Your DMP should inform what you say in your ethics application about how you will collect, store and re-use your data.  It is important that your DMP and your ethics application are in agreement and you provide your participants with the correct information. Once you receive your ethics approval, review your data management plan and update as necessary.

Reviewing your Data Management Plan

A DMP should be a living document and should be updated as your research develops.  It should be reviewed on a regular basis and good practice would encourage that the dates of review are included in the plan itself.  Use of a version table in any document can be helpful.

What data will be created?

In your data management plan you need to provide some detail about the material you will be collecting to support your research.  This should cover how you will collect notes, supporting documentation and bibliographic management as well as your primary data. Will all your data be held electronically or will you require to maintain a print notebook to collect your observations?

Are you using Secondary Data?

Not everyone has to collect their own data, it may already have been collected and made available.  This data is known as secondary data.  Some secondary data are freely available, but other data are released with terms and conditions that you need to meet.  In some cases this may influence where you can store and analyse the data.  You need to be aware of this as you plan the work you intend to do.

How are you collecting or creating your data?

How you collect or gather the material for your research will influence what you need to do to manage them. The way you do this may alter as your research progresses and you should update your plan as required. Will you be collecting data by observing, note-taking in an archive, carrying out experiments or a mixture of these? 

How much data are you likely to have?

Knowing how much data you might create is important as it will dictate where you can store your data and whether you need to ask for additional storage from iSolutions.  It is unlikely that you can say exactly what volume of data you might create, but you will have an idea of individual file sizes.  If you will be working with word, excel documents and a reference management software library then you are likely to be dealing with megabytes or gigabytes of data. If you will be collecting high resolution images then you may end up needing to store terabytes. Estimate as early as possible and if you think you may need additional space you should discuss this with your supervisor.

What formats will you be using?

A crucial factor in being able to share data is that it is in an open format or collected using disciplinary standard software that allow export to open formats.  Consider how open the format of your data will be when selecting the software, instruments, word processing packages that you use. See the Data formats section in  Introducing Research Data  Part III for points to consider.

Who will own the data?

If you have been sponsored by a research council, government, industry or commercial body the agreement you signed may cover ownership of the data that you create.  Being aware of this early is useful as it will influence what you are able to do when you come to writing papers, sharing and depositing your data when your finish. It may also impact on where you can store your data.  

How will you make your data findable?

Using standards to capture the essential metadata is a good way to help create data that will be easy to find.  It will also make preparing for deposit in the future more straightforward.  The Research Data Alliance has a helpful list of  disciplinary metadata  and use case examples.  You can make reference to these in your plan once you know what will be most appropriate to use.

Where will you store the data during your PhD?

Where you store your data will depend on things such as the type and size of data you are collecting.  Certain types of data, such as personal , special category data (formerly referred to as sensitive data) or commercially confidential data, will require to be stored more securely than others.  This type of data generally requires to be stored on University network drives that have additional protection and not on personal computers or cloud storage (for example, Office 365, One Drive). Where you are collecting less sensitive data your choice of storage is wider.  For all storage it should in a location with good back-up procedures in place. Consult iSolutions knowledge base  for further information.

How will you name your files and folders?

It can be helpful to think about creating a procedure on how you will name your files. This is a basic step where it is useful to consider how easy it will be to interpret the name in the future.  Abbreviations can be good, but ask yourself how someone else might understand the file name should you need to share it with them. What would make it easy to know what each file contains?  While it is possible to have quite longer file names this can cause problems when you zip files. 

How will you tell one version of a file from another?

How will you be able to tell whether you are dealing with the latest version of a file? How will you manage major versus minor changes?  What if you want to return to an earlier version?  Use the data management plan to investigate what would be the optimum method for you and establish a good procedure from the beginning.  Generally the use of 'draft', 'latest' or 'final' should be avoided.  Instead consider using the data (YYYY-MM-DD) or a version number, for example, v.1.0  where the nominal value increases with major changes and decimal for minor ones.  Adding a version table at the end of a document can also be helpful.

How can you share your data?

To make data accessible is not about doing something at the end of the project, but needs to be planned for from the beginning.  During your research you are likely to have colleagues or collaborators who will need to be able to access the data - how will you do this?  Will you need a collaborative space and if so what can you use?  Does it need to be is a protected location with restricted access due to the type of data you are using? By establishing good procedures on documentation, metadata collection, file-naming and using disciplinary standards this will assist you throughout your research, as well as helping at the end.

How do you handle personal, sensitive or commercially confidential data?

If the data you are collecting contains   personal ,  special category  data (formerly referred to as sensitive data) or commercially confidential data  then sharing or transferring the files needs to be carried out in a way that does not make the data vulnerable. Data should be anonymised or pseudo-anonymised as early as possible after collection, seek disciplinary guidance prior to collection. 

The medium of transfer must be secure and where necessary encryption should be used. You may want to consider one of the following:

There may be other software available and you should check if there is a standard in your discipline. 

Transferring data via USB or external drives is not recommended, but where required these should be encrypted. Avoid using email to send files and instead use our University SafeSend service.  This offers transfer of files up to 50GB and your files can be encrypted by ticking "Encrypt every file" when creating a new drop-off - see ' How secure is SaveSend'

What data do you need to keep and what do you need to destroy?

Not all the data from a project needs to be kept and the data you collect should be reviewed regularly.  The Digital Curation Centre (2014) guide  ' Five steps to decide what data to keep: a checklist for appraising research data v.1 ' may help you to decide what to retain. It is important that you retain or discard data in line with your ethics approval.

You also need to consider what data needs to be destroyed, how you will mark the data for destruction and when this needs to happen. Destroying paper based records is relatively easy through our confidential waste system.   Destroying digital data is less so as it may need to be done so that it cannot be forensically recovered. Guidance on destroying your data is available  or contact iSolutions for advice.

Why do you need to consider the long-term storage now?

At the end of your PhD you will be encouraged to share your data as openly as possible, and as closed as necessary. To do this safely consider what you need to do to enable your data to be accessible in the future.  Knowing where the best place to store your data may inform what you need to plan for in its creation or collection.  Are you aware of any disciplinary data repositories that hold similar data?  Examples are:

• Archaeology - Archaeology Data Service  
• ESRC - UK Data Archive
• STFC -  eData   
• NERC - data centres
• Biology - GenBank
• General repository - Zenodo

Investigate what requirements these repositories have on formats, documentation etc and incorporate these into your plan. Otherwise you should plan to deposit in the University Institutional Repository . 

There are currently no costs for depositing most dataset in our Institutional Repository unless the data requires specialist archive storage or is in excess of 1TB. External repositories may have charges for depositing data. 

Who will be creating the archive?

Generally as a PhD the job of drawing together your data into a dataset ready for deposit will fall to you as the researcher.  It is not the responsibility of your supervisor, although they may be able to advise on what needs to be done.  If you are part of a larger project there may be someone designated to curate the project data.  For further assistance contact [email protected] . 

How long should the data be kept?

This will depend on a number of factors.  Your funder may have a policy that requires the data to be held for a minimum of 10 years from last use.  If you are working in certain medical areas the data may need to be held for 25 years.  There may be some restrictions on how long you can retain personal data relating to Data Protection Act 2018 (GDPR).  Significant data that has been given a persistent identifier (DOI) will be kept permanently.

What documentation or additional information needs to accompany the data?

Keeping a record of what changes you have made, when data was collected, where data was collected from, observations, definitions of what has been collected are all crucial to allowing data to be used safely and with integrity. How do you plan to do this? How will you make sure that you can match up your notes with the files they refer to?  Some programming languages such as Python and R allow you to make notes in the files about what you are doing which is really helpful.  Where this is not an option then you will need to develop your own method to make sure that processes applied to the data are recorded and available to you to refer back to later.  Creating a register of your files by type using an excel spreadsheet may be worth considering, but it should be manageable and importantly kept up-to-date.

In order for data to be reusable it requires data provenance.  Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures.

• Research Data Management and Sharing - Documentation The importance of systematically documenting your research data. more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

What restrictions will need to apply?

Not all data can be made openly available.  Some data may only be shared once a data sharing agreement has been signed, while other data may not be suitable for sharing.  Funding councils encourage all data to be as open as possible and as closed as necessary. Where will your data fit with this?  What agreements do you need to be able to share your data?

When can data be made available?

Data can be deposited in our Institutional Repository  and kept as an 'entry in progress' until it is ready for publication. 

Not all data needs to be made immediately available at the end of your PhD.  It is possible to add an embargo to give yourself some additional time to find funding to continue your work and re-use your own data.  See Regulations on embargoes.

However, it is not always necessary for you to wait until the end of your PhD before depositing data.  If you write a conference or journal paper it is likely that you will be asked to make the underpinning data available.

How will you keep your data safe?

What would happen if your files became corrupted or your laptop was stolen, would you be able to restore them?  What would happen if someone was able to access your data without your knowledge or approval?  If you are holding personal  or   special category  data (formerly referred to as sensitive data) and these became public this would be a data breach with potentially serious consequences.

Dr Fitzgerald  Loss of seven years of Ebola research  

Consider carefully the impact to you and your research if these were to happen and what procedures you may need to put into place to reduce the risk of these happening.

• Research Data Management and Sharing - Data Security Ensuring your research data are kept safe from corruption, and that access is suitably controlled more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

How will you back up your data?

Good housing keeping of your data is important and this includes doing regular back ups of your data.  University storage is backed up regularly but it is important to have your own 'back up' folders, kept separately from your working files.  Back up should be done on as regular a basis as required.  This can be defined by the length of time you are prepared to repeat work lost.  You may need to back up daily, weekly or monthly depending on the nature of your research.  

• Research Data Management and Sharing - Backup Effective backup strategies for your research data. more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

As well as establishing a process for backing up your files, you should check the process of restoring your files.  You will need to check that the files restore correctly.  Having good documentation on what your files contain, what transformations or analysis has been carried out will be invaluable for this process.

How can you safely destroy data?

Destroying data, especially   personal ,  special category  data (formerly referred to as sensitive data) or commercially confidential data , is not as straightforward as just deleting the file.  Further action is required otherwise the data could be recovered.  Please read our guidance on destruction of data   and GDPR regulations .

• Data Disposal Essential guidance from the UK Data Archive on data disposal

An important part of research data management is that your plan is implemented and part of your everyday good research practice.  The plan should be a living document and reflect your practice.  You may find that some parts become redundant or that there is a better way to carry out a process so your plan should be updated. As a PhD researcher it is likely that you will be the person responsible for implementing the plan.  If your research is part of a wider research project there may be someone in the team who has been given the role and you should discuss your data management plan with them.

Having written your plan consider what actions do you need to take in order to carry it out? What further information do you need to find? Investigate what training or briefing sessions are available via PGR Manager.  If you want to enhance your data analysis skills check out material on Linked in Learning 

Over time we will add plans to this section as we get permission to share them.

• PhD DMP Example (Web Science) This is an example PhD Data Management Plan for a research project looking at learner engagement and peer support in digital environments.
• Arts and Humanities
• Science, Medicine and Engineering
• Social Sciences
• Further Reading

Courses offered by the University:

Data Management Plans for Doctoral Students -  mandatory course on for all new doctoral students. Log into Blackboard using your university username and password.

Data Management Plan: Q&A Clinic - as a follow up to the compulsary online course, the Library is running twice weekly clinics to answer your DMP queries. Book PGR Development Hub .

Data Management Plan: Why Plan?  45 minute briefing.  A Panopto recording of this course  is  available

Research Data Management: What you need to know from the start .  45 minute briefing. Book via Gradbook

Research Data Management Workshop .180 minute workshop Book via Gradbook

• Introduction to research data (visual arts) Introduction to research data in the visual arts, wirtten by Marie-Therese Gramstadt as part of the Kultur project
• Manage, improve and open up your research and data PARTHENOS training module on various aspect on data management
• VADS4R Data Management Planning A toolkit developed by the Visual Arts Data Skills for Researchers (vads4R)
• Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics The Cross-Linguistic Data Formats initiative proposes new standards for two basic types of data in historical and typological language comparison (word lists, structural datasets) and a framework to incorporate more data types (e.g. parallel texts, and dictionaries). The new specification for cross-linguistic data formats comes along with a software package for validation and manipulation, a basic ontology which links to more general frameworks, and usage examples of best practices. [article]
• EMBL-EBI Training EMBL-EBI train scientists at all levels to get the most out of publicly available biological data.
• Datatree - Data Training A free online course, aimed at PhD and early career researchers, with all you need to know for research data management, along with ways to engage and share data with business, policymakers, media and the wider public. more... less... The course is for any scientist, whether you look after your own data or are guided by an organisation.
• Expert Tour Guide on Data Management A guide for social science researchers who are in an early stage of practising research data management.
• CESSDA ERIC RDM User Guides Brief guides on important topics in data management and a helpful checklist
• Guide to Social Science Data Preparation and Archiving An important guide covering the different stages of data management to enable the sharing and preserving of data in the Social Sciences
• Managing your dissertation data : Thinking ahead Maureen Haaker and Scott Summers from the UK Data Service gave this presentation. The session sought to help the students ensure transparency in the collection and writing up of their dissertation, whilst also ensuring that good practices in data management were followed. more... less... Although aimed at undergraduate dissertation it provides useful information for everyone.
• UK Data Service Prepare and Manage Data Good data management practices are essential in research, to make sure that research data are of high quality, are well organised, documented, preserved and accessible and their validity controlled at all times. This results in efficient and excelling research.
• FAIR Principlies Guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets
• How to Develop a Data Management and Sharing Plan Jones, S. (2011). ‘How to Develop a Data Management and Sharing Plan’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/how-guides
• MRC Retention framework for research data and records guidance on retention of research data an records resulting from population health and clinical studies
• Open Data Handbook Handbook that discusses the why, what and how of open data – why to go open, what open is, and the how to ‘open’ data.
• Open Research Data and Materials Open Science Training Handbook section on research data

Key Documents

• DMP Templates
• Deposit Guide
• Code of Conduct for Research University of Southampton policy - October 2017
• Data Protection Policy University of Southampton policy May 2018
• Data Sharing Protocol University of Southampton protocol - May 2018. [Login required]
• Ethics - Human participant policy University of Southampton policy - March 2012
• Ethics - Policy on Cultural Heritage University of Southampton policy - October 2018
• Research Data Management Policy University of Southampton policy - 2015

The template below has been provided to assist you in writing your data management plan.  Not all sections will be relevant, but you should consider carefully each section.

• Template for PhD DMP (pdf)
• Template for PhD DMP (Word)

When the time comes to deposit your data, follow the advice in our Thesis Data Deposit guide . 

Email us on: [email protected]

Who's Who in the Research Engagement Team

Research Support Guide

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Unlocking the Secrets of Effective PhD Data Collection: Strategies, Methods, and Best Practices

When embarking on the exciting journey of pursuing a PhD, one of the critical aspects that researchers must master is the art of data collection. The success of any thesis hinges upon the accuracy, relevance, and reliability of the collected data, making it essential to unlock the secrets of effective PhD data collection. In this comprehensive blog, we will explore a range of strategies, methods, and best practices to ensure that your thesis data collection process is conducted meticulously and yields valuable insights. By harnessing these invaluable insights, you will be equipped to make informed decisions, draw meaningful conclusions, and contribute significantly to your field of study. So, let's dive into the world of thesis data collection, uncovering the strategies and methodologies that will elevate the quality and impact of your research.

Types of Research Data

In the realm of research, data serves as the foundation upon which discoveries are built and theories are tested. Understanding the various types of research data is crucial for designing appropriate data collection methods and effectively analyzing the information gathered. Here are some common types of research data:

Quantitative Data : This type of data is expressed in numerical form and can be measured objectively. It involves collecting information through methods such as surveys, experiments, or structured observations. Examples of quantitative data include measurements, counts, ratings, and statistical data.

Qualitative Data : Unlike quantitative data, qualitative data is descriptive and focuses on capturing the richness and depth of experiences, opinions, and behaviours. It is collected through methods such as interviews, focus groups, observations, or analysis of textual or visual materials. Qualitative data provides insights into attitudes, motivations, perceptions, and social constructs.

Primary Data : Primary data is original data collected firsthand by researchers specifically for their research objectives. It involves gathering data directly from participants or sources through surveys, interviews, experiments, or observations. Primary data is tailored to the specific research questions and provides unique insights into the research problem.

Secondary Data : Secondary data refers to existing data that has been collected by someone else for a different purpose but can be used for research purposes. This data can be obtained from various sources such as government agencies, research organizations, published literature, or online databases. Examples of secondary data include census data, academic journals, reports, or archival records.

It is important to select the appropriate data type for your research objectives and design your data collection methods accordingly. Integrating multiple types of data can provide a comprehensive understanding of the research problem and enhancing the validity and reliability of your findings.

Range of strategies

To ensure that your thesis data collection process is conducted meticulously and yields valuable insights, here are some strategies to consider:

Clearly Define Research Objectives : Begin by clearly defining your research objectives and questions. This will guide your data collection efforts and ensure that the collected data aligns with your research goals. Clearly defined objectives help focus your data collection process and maintain consistency throughout.

Choose Appropriate Data Collection Methods : Select data collection methods that align with your research objectives and the type of data you intend to collect. Common methods include surveys, interviews, observations, experiments, or analysis of existing data sources. Consider the strengths and limitations of each method and choose the most suitable ones for your research.

Develop a Detailed Data Collection Plan : Create a comprehensive plan that outlines the step-by-step process of data collection. This plan should include details such as the target population, sample size determination, data collection tools, timeline, and any necessary ethical considerations. A well-defined plan ensures systematic and organized data collection.

By implementing these strategies, you can conduct your thesis data collection process meticulously, ensuring that the data collected is robust, and reliable, and provides valuable insights for your research.

Range of methods 

To ensure that your thesis data collection process is conducted meticulously and yields valuable insights, consider implementing the following methods:

Sampling Techniques : Carefully choose appropriate sampling techniques to ensure that your sample represents the target population. Random sampling, stratified sampling, or purposive sampling can be employed based on the nature of your research and the availability of participants. Proper sampling methods help minimize bias and increase the generalizability of your findings.

Structured Data Collection Instruments : Design and utilize well-structured data collection instruments such as surveys, questionnaires, or interview guides. Ensure that the instruments are clear, concise, and relevant to your research objectives. Use standardized scales and response options to facilitate data analysis and comparison. Pilot testing and obtaining feedback from experts can enhance the quality of your instruments.

Data Triangulation : Employ data triangulation by utilizing multiple data collection methods or sources. This involves gathering data from different perspectives or using different methods to validate findings. For example, combining survey responses with interviews or incorporating existing data sources can provide a more comprehensive and robust understanding of the research topic.

By utilizing these methods, you can conduct your thesis data collection process meticulously, maximizing the value of the insights gained and strengthening the validity and reliability of your research findings.

Range of best practices

To ensure that your thesis data collection process is conducted meticulously and yields valuable insights, it is important to follow these best practices:

Thoroughly Plan and Prepare : Start by developing a detailed data collection plan. Clearly define your research objectives, research questions, and variables of interest. Determine the appropriate data collection methods, sampling techniques, and data analysis approaches. Adequate planning and preparation set the foundation for a successful data collection process.

Obtain Ethical Approval : If required, obtain ethical approval from your institution's research ethics board. Adhere to ethical guidelines and ensure that your data collection process respects the rights, privacy, and confidentiality of participants. Obtain informed consent and provide necessary information about the research objectives and participant rights.

Pilot Test and Refine : Conduct a pilot test of your data collection instruments or methods before implementing them on a larger scale. This helps identify any potential issues, ambiguities, or flaws in the instruments. Based on the pilot test feedback, refine and improve your data collection tools to enhance their effectiveness and clarity.

By adhering to these best practices, you can ensure that your thesis data collection process is meticulous, reliable, and yields valuable insights, contributing to the credibility and significance of your research.

Practical applications

Some practical applications of effective PhD data collection include:

Research studies : Effective data collection methods enable PhD researchers to gather relevant and accurate data for their research studies. This data can be used to analyze trends, test hypotheses, and draw meaningful conclusions.

Surveys and questionnaires : Collecting data through surveys and questionnaires allows researchers to gather information from a large number of participants. This data can be used to understand opinions, attitudes, and behaviors, providing valuable insights for research purposes.

Fieldwork and observations : For PhD research that involves fieldwork or observations, effective data collection is crucial. It allows researchers to systematically gather data in real-world settings, providing valuable context and rich information for their studies.

Experimental research : In experimental research, effective data collection ensures that all relevant variables are measured accurately. This enables researchers to evaluate the impact of interventions or treatments and draw valid conclusions about cause-and-effect relationships.

Longitudinal studies : Longitudinal studies require collecting data over an extended period. Effective data collection methods allow researchers to gather data at different time points, enabling the examination of changes, trends, and developments over time.

Qualitative research : Effective data collection is vital for qualitative research methods such as interviews, focus groups, or case studies. It ensures that researchers capture in-depth insights, experiences, and perspectives of participants, contributing to a comprehensive understanding of the research topic.

Literature reviews : Data collection in the form of literature reviews involves gathering relevant published studies, articles, and other sources of information. Effective data collection methods help researchers identify and select appropriate sources, ensuring a comprehensive and reliable review.

Hence, effective data collection methods are essential across various research domains and can contribute to producing robust, reliable, and meaningful findings during the course of a PhD program.

In conclusion, unlocking the secrets of effective PhD data collection is a critical endeavor that requires careful planning, strategic implementation, and adherence to best practices. The process of data collection is the backbone of any research, and by employing appropriate strategies, methods, and best practices, researchers can maximize the quality and value of their findings. The meticulous execution of data collection ensures that the collected data is robust, reliable, and capable of providing valuable insights into the research questions at hand. By integrating thorough planning, ethical considerations, rigorous training, and continuous monitoring, researchers can overcome challenges and optimize the data collection process. Maintaining data integrity, quality assurance, and transparency further strengthens the credibility and significance of the research outcomes. Ultimately, effective data collection serves as the foundation for rigorous analysis, meaningful interpretations, and advancements in knowledge within the realm of PhD research.

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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.

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