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• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

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, other interesting articles, 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 analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

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 (e.g., determine inclusion and exclusion criteria ).

Operationalization

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

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

• You 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 timeframe of the data collection.

Standardizing procedures

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

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

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

Creating a data management plan

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

• If you are collecting data from people, you will likely need to anonymize 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 minimize distortion.
• You can prevent loss of data by having an organization 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 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

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

• 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.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

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

When conducting research, collecting original data has significant advantages:

• 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 standardize 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, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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

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

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

• 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.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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How to collect data for your thesis

Collecting theoretical data

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

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

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

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

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

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

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

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

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

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

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

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

Some popular content sharing sites are:

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

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

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

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

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

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

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

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

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

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

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

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Data Collection – Methods Types and Examples

Table of Contents

Data Collection

Definition:

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

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

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

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

Secondary Data Collection

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

Qualitative Data Collection

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

Quantitative Data Collection

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

Data Collection Methods

Data Collection Methods are as follows:

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

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

Focus Groups

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

Observation

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

Experiments

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

Case Studies

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

Secondary Data Analysis

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

How to Collect Data

The following are some steps to consider when collecting data:

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

Applications of Data Collection

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

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

Examples of Data Collection

Examples of Data Collection are as follows:

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

Purpose of Data Collection

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

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

When to use Data Collection

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

Data collection is particularly useful in the following scenarios:

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

Characteristics of Data Collection

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

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

Advantages of Data Collection

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

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

Limitations of Data Collection

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

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

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Methods of Data Collection – Guide with Tips

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

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

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

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

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

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

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

Primary Research Strategy

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Questionnaires

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

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

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

Observation

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

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

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

Focus Groups

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

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

What data collection best suits your research?

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Secondary Research Strategy

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

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

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

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

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

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

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

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

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

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

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

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

Frequently Asked Questions

What are the different methods of data collection.

Different methods of data collection include:

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

What is data collection?

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

What methods of data collection are there?

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

Which example illustrates the idea of collecting data?

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

What is qualitative data?

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

How to collect data?

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

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

This post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision.

Experimental research refers to the experiments conducted in the laboratory or under observation in controlled conditions. Here is all you need to know about experimental research.

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11 Tips For Writing a Dissertation Data Analysis

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

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

What is Data Analysis in Dissertation?

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

Data Analysis Tools

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

11 Most Useful Tips for Dissertation Data Analysis

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

1. Dissertation Data Analysis Services

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

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

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

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

2. Relevance of Collected Data

3. data analysis.

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

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

4. Qualitative Data Analysis

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

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

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

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

5. Quantitative Data Analysis

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

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

6. Data Presentation Tools

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

7. Include Appendix or Addendum

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

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

8. Thoroughness of Data

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

9. Discussing Data

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

10. Findings and Results

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

11. Connection with Literature Review

The role of data analytics at the senior management level.

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

The Decision-Making Model Explained (In Plain Terms)

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

13 Reasons Why Data Is Important in Decision Making

Wrapping up.

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

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

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Guide for Thesis Research

• Introduction to the Thesis Process
• Project Planning
• Literature Review
• Theoretical Frameworks
• Research Methodology
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Basics of Methodology

Research is a process of inquiry that is carried out in a pondered, organized, and strategic manner. In order to obtain high quality results, it is important to understand methodology.

Research methodology refers to how your project will be designed, what you will observe or measure, and how you will collect and analyze data. The methods you choose must be appropriate for your field and for the specific research questions you are setting out to answer.

A strong understanding of methodology will help you:

• apply appropriate research techniques
• design effective data collection instruments
• analyze and interpret your data
• develop well-founded conclusions

Below, you will find resources that mostly cover general aspects of research methodology. In the left column, you will find resources that specifically cover qualitative, quantitative, and mixed methods research.

General Works on Methodology

Qualitative Research

Quantitative Research

Mixed Methods Research

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• Last Updated: Jan 23, 2024 4:31 PM
• URL: https://library.guilford.edu/thesis-guide

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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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.

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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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• Proquest Theses and Dissertations (PQDT), a database of dissertations and theses, whether they were published electronically or in print, and mostly available for purchase. Access to PQDT may be limited; consult your local library for access information.

Data Collection Methods

Regardless of the topic of your dissertation or thesis, it is highly likely that at some point you will need to collect data. Below are some common data collection methods. Remember, you will want to collect data in a way that fits your research design and questions.

Self-Report

Self-report is a type of research design in which participants give their responses to a given set of questions. The most common types of self-report are interviews or questionnaires. One major limitation of self-report versus other data collection methods is that accuracy of responses cannot be determined, and there are many circumstances in which participants are likely to lie.

Observation

Observation is a method of collecting data in which members of research teams observe and record behaviors. Data collected during observation are explicit and quantifiable. However, observation has many limitations. First, researchers who use observation can only observe behaviors; therefore, observation cannot be used to collect data about attitudes, beliefs, thoughts, covert behaviors, etc. Another limitation of observation is that it is a known fact that being observed changes behavior. Observation can be either formal (e.g., structured in a laboratory setting) or casual (e.g., in the natural environment), and the observer may either be a participant (e.g., member of the group being observed) or a nonparticipant (e.g., not a member of the group being observed).

Physiological Measures

Physiological measures can be used to collect data related to the body, such as heart rate, fMRI, EEG, CAT, breathing rate, etc. These types of data are useful because they are quantifiable and accurate. However, these types of data are sometimes used as secondary measures of latent constructs, which may not always be accurate. For example, someone with a high heart rate may be perceived as being anxious, but it is possible that that person just walked up a flight of stairs.

Interviews are one of the data collection methods for qualitative research. Interviews consist of meeting with participants one on one and asking them open-ended questions. Interviews can be structured or semi-structured. In a structured interview, the researcher has a predetermined set of questions to ask and does not deviate from those questions. In a semi-structured interview, the researcher will have prepared questions but has the freedom to ask additional follow up questions as he or she sees fit.

Focus Groups

Focus groups are another example of data collection methods of a qualitative study. Using focus groups to collect data is similar to using interviews because focus groups allow participants to freely answer questions; however, as implied by the name, focus groups consist of multiple people all being asked questions at the same time.

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CHAPTER 3 - RESEARCH METHODOLOGY: Data collection method and Research tools

As it is indicated in the title, this chapter includes the research methodology of the dissertation. In more details, in this part the author outlines the research strategy, the research method, the research approach, the methods of data collection, the selection of the sample, the research process, the type of data analysis, the ethical considerations and the research limitations of the project. The research held with respect to this dissertation was an applied one, but not new. Rather, numerous pieces of previous academic research exist regarding the role of DMOs in promoting and managing tourist destinations, not only for Athens in specific, but also for other tourist destinations in Greece and other places of the world. As such, the proposed research took the form of a new research but on an existing research subject. In order to satisfy the objectives of the dissertation, a qualitative research was held. The main characteristic of qualitative research is that it is mostly appropriate for small samples, while its outcomes are not measurable and quantifiable (see table 3.1). Its basic advantage, which also constitutes its basic difference with quantitative research, is that it offers a complete description and analysis of a research subject, without limiting the scope of the research and the nature of participant’s responses (Collis & Hussey, 2003).

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As the tourism sector is continually evolving, touristic destinations and service providers should give close and thoughtful attention to customers' satisfaction, particularly during the Covid-19 pandemic period. Tourism for Greece represents one of the most valuable pillars of the economy and the impact of the pandemic to the sector and GDP will be significant. In this era, it is evident the importance of the Sustainable Development Goals and effective Destination Management that will take into consideration all aspects of the local communities. Customer satisfaction is crucial to improving strategies that destinations must follow to service quality and satisfaction management strategies. Recent consumer and technological trends make customer satisfaction more important than ever. This paper aims to investigate the characteristics, preferences, images, satisfaction levels, and the overall experience gained by the tourists visiting Lesvos island in the North Aegean Region Greece. Primary research was conducted and the airport of the island during departure in 2019. The useful gathered questionnaires (201) provided helpful information to the island's DMO related to the visitors' demographic characteristics, destination perception, awareness and competitiveness, satisfaction and overall experience. The basic research findings were the strong impression of the visitors about the authenticity of the destination. They also believe that prices are excellent and the rate of value for money is high. At the same time, visitors think that the island is not promoted very good and the image/brand of the island is not very clear and well defined. It is the first research conducted to visitors departing from Lesvos island to the authors' best knowledge. The results and discussion of this study will be useful to the islands' DMO and the island's tourism authorities and the North Aegean Region and other similar island destinations, which wish to maximize the benefits of tourism development.

Spyros Langkos

DOI: 10.13140/2.1.3231.1683 INDEPENDENT STUDΥ - THESIS " Athens as an international tourism destination: An empirical investigation to the city’s imagery and the role of local DMO’s.” The aim of this project was to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens. The result of this study indicated that DMOs are playing a crucial role for the promotion of Athens as a tourist destination. DMOs key responsibilities include: development of sophisticated online marketing strategies, creation of high quality published material, participation in international tourism fairs for developing relationships with key stakeholders and development of network synergies with airline companies, and international tourism organizations. Athens is a destination with great potential for future growth and for that reason DMOs have designed certain plans for the next three years in order to exploit the opportunities which are presented. The future plans of the DMOs give particular emphasis in the opening in new tourist markets and more particularly in the markets of Russia, Turkey China, and USA. Besides, DMOs will focus in five forms of tourism which can be developed successfully in Athens, namely: 1) cultural tourism, 2) health tourism, 3) luxury tourism, 4) city break tourism, and 5) convention tourism On the other hand, the executives of the DMOs underlined several problems which prevent the tourism development of Athens. The majority of these problems are related with the business environment in Greece which has become less competitive due to the crisis. Besides, the city as a destination faces the problems of seasonality as well as missing infrastructures. Finally, the research showed that DMOs have established strong and long term relationships with DMOs in foreign countries. These partnerships allow the Greek DMOs to be updated concerning the trends of the global tourism market as well as enhance the movement of tourists between cooperating countries. Nevertheless, the promotion of Athens as a tourism destination requires a more concerted effort between the public and the private stakeholders which are involved in the tourism industry. The benefits will be multiplied for businesses, the state and the society in general. Keywords & terms: Destination Marketing Organizations, DMO’s, tourism destination, tourist product, popularity & imagery, interviews, online marketing strategies, Athens, Greece, international tourism fairs, stakeholder relationships, network synergies, airline companies, future growth, tourist markets, cultural tourism, health tourism, luxury tourism, city break tourism, convention tourism, tourism development of Athens, business environment in Greece, seasonality, infrastructures

HOTELARIA & TURISMO UNIV ALGARVE, PORTUGAL

Aan Jaelani (SCOPUS ID: 57195963463)

Dear Participant, I am Spyros Langkos and I am collecting data from you which will be used in my dissertation for: Athens as an international tourism destination. An empirical investigation to the city’s imagery and the role of local DMO’s, as part of my MSc in Marketing Management at the University of Derby. The objective of the dissertation research, will be to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing and the information you will be asked to provide will be used to help to provide insights to achieve this objective. The data you provide will only be used for the dissertation, and will not be disclosed to any third party, except as part of the dissertation findings, or as part of the supervisory or assessment processes of the University of Derby. The data you provide will be kept until the 31st of December 2014, so that it is available for scrutiny by the University of Derby as part of the assessment process. If you feel uncomfortable with any of the questions being asked, you may decline to answer specific questions. You may also withdraw from the study completely, and your answers will not be used. And, if you later decide that you wish to withdraw from the study, please write to me at Spyros Langkos, email: [email protected] no later than the 30th of March 2014 and I will be able to remove your response from my analysis and findings, and destroy your response. The Researcher Spyros Langkos

Gregory T Papanikos

This abstract book includes all the summaries of the papers presented at the 9th Annual International Conference on Tourism 10-13 June 2013, organized by the Sciences and Engineering Research Division of the Athens Institute for Education and Research. In total there were 34 papers and 45 presenters, coming from 19 different countries (Australia, Canada, China, Cyprus, Egypt, Hong Kong, Hungary, Ireland, Israel, Lithuania, Poland, Portugal, South Africa Spain, Taiwan, Turkey, UAE, UK, USA). The conference was organized into IX sessions that included areas of Tourism Marketing Issues, Tourism Destination and Development, Special Tourism Themes Entrepreneurship, Economics and Business in the Tourism Industry and other related fields. As it is the publication policy of the Institute, the papers presented in this conference will be considered for publication in one of the books of ATINER.

The tourism industry in Greece is one of the most important sectors of the country’s economy it terms of value (Hellenic Statistical Authority, 2014). There are several public and private organizations which are involved in the tourism industry in Greece for promoting destinations such as the Destination Management Organizations (DMOs). In this context, the aim of this project is to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing. More specifically, the project has the following objectives:  To identify the activities which are performed by DMOs for promoting Athens and to evaluate the strategic role of DMO’s.  To identify the importance of destination marketing through its application in the Greek Tourism Industry and the particular case of Athens.  To portrait the opinions and activity planning of Greek DMO’s Executives, who are considered to be experts in the tourism field.  To provide insights and new trends of high informational value about the Tourism Industry in Athens.  To highlight the latest incentives and programming concerning the city’s future developments.  To identify the key problems that Athens faces as a tourist destination and to recommend points for improvement from the DMOs perspective.

The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens.

Annals of Tourism Research

This paper examines the directions and methodological practices of tourism research carried out in Greece over the last three decades, highlights critical issues in the developmental path of this study in the country, and makes certain proposals concerning its future orientation. Although the reviewed published works are informed by disciplinary methods and theories, it is the anthropological approach that guides this paper’s interpretations. Further, a preliminary analysis of tourism representations illuminates the local-global relationships. In addition, this exploration demonstrates how Greece’s tourism identity is shaped by powerful discourses embedded in historical, political, and ideological processes.Les recherches de tourisme au sujet de la Grèce. Cet article examine les directions et les pratiques méthodologiques des recherches de tourisme qui ont été réalisées en Grèce dans les trente dernières années, souligne des questions critiques dans le chemin de développement de cette matière dans ce pays et fait certaines suggestions au sujet de son orientation future. Quoique les ouvrages publiés qui sont examinés sont fondés sur des méthodes et des théories disciplinaires, c’est une approche anthropologique qui guide les interprétations de cet article. De plus, une analyse préliminaire des représentations du tourisme éclaire les relations globales/locales. Et cette exploration démontre comment l’identité du tourisme en Grèce est formulée par des discours puissants qui font partie des processus historiques, politiques et idéologiques.

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

Qualitative Research: Data Collection, Analysis, and Management

Introduction.

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

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

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

THE PARTICIPANT’S VIEWPOINT

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

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

DATA COLLECTION

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

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

DATA ANALYSIS AND MANAGEMENT

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

Interpretation of Data

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

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

Transcribing and Checking

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

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

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

Reading between the Lines

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

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

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

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

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

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

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

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

DATA SYNTHESIS

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

Planning and Writing the Report

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

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

CONCLUSIONS

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

Appendix 1. Excerpt from a sample transcript

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

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

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

Previous articles in this series:

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

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

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

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

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

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

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

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

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

Competing interests: None declared.

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 ]

University Libraries

Research methods for social sciences.

• Research Philosophy
• Literature Review
• Research Design
• Data Collection
• Data Analysis and Reporting
• Beyond the Traditional Methods
• Research Ethics

Introduction

As part of your research plan and design, you will select a data collection method to address your research problems. This page provides information on quantitative, qualitative, and combined methods.

If you are planning to use an exisiting dataset from other researcher or organization, visit the Finding Datasets guide for information of public datasets, data platforms available through the UNT Libraries, and analytic tools available to use directly from certain data providers.

Quantitative Data Collection

Quantitative methods to collect data involve measures and numerical information that can be further tested and analyzed with statistical methods. The most common forms of quantitative data collection methods are:

• Experiments
• Observation with instruments
• Spatial data
• Surveys with numerical scaled questions

Below are some resources from the UNT Libraries that provide guidance on quantitative data collection methods and sampling techniques commonly used in social science research.

Qualitative Data Collection

Qualitative data collection focuses on collecting information based on experience, thoughts, and feelings from your subjects or representations from artifacts in your discipline. The most common ways to collect qualitative data are:

• Examining artifacts (e.g. text, documents, images, video, audio, objects)
• Holding focus Group
• Conducting interviews
• Observing phenomena
• Conducting surveys with open-ended questions

Below are some resources from the UNT Libraries that provide guidance on qualitative data collection methods and sampling techniques commonly used in social science research.

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Francesco Lelli

Where to get data: a collection of resources for your thesis.

If you are wondering where to get data for your thesis this article is for you.

Data come in all shapes and forms. If you are doing your thesis, you are in search of a proof of concept In other words, you are attempting to prove the validity of an idea or concept, not to produce an industry/ready solution . Therefore, most of the time, you do not need particularly large datasets. However, you want to be sure that they are of a sufficient quality. After all, you need to elaborate them without introducing too much noise.

What follows is a selected set of resources. It is not exhaustive and I am pretty sure that you will be able to find more by searching the Internet and asking your supervisor. However, this list is a good starting point in case you are trying to build a research/thesis proposal or you are stuck and in search of an idea.

Google datasets search:

• Link: https:// datasetsearch.research.google.com /

You guessed right. Google has a dedicated search engine for datasets. It is freely available and index data that implement a particular schema.org format. Some of the data may be behind paywalls. However, academics and students usually can contact the data provider in order to get access to them or to a selected portion that is sufficient for their study.

EU Open Data Portal:

• Link: https://data.europa.eu/euodp/en/data/

Once again, you guessed right. The European Union is committed to foster a strong data transparency policy. At the time of writing this article, I was able to find 15399 different datasets that are freely available. They cover a large variety of topics and are related to all the various arguments under the jurisdiction of the EU.

Yahoo Finance:

• Link: https://finance.yahoo.com/
• Link to the help center: https://help.yahoo.com/kb/download-historical-data-yahoo-finance-sln2311.html

There are plenty of financial DB in the web. Yahoo finance is the most known and straightforward. In case you are looking for daily quotations of various financial assets, this is the place for you. The second link will explain how to download historical data.

More on financial data:

• Link Thomson Reuters API: https://customers.reuters.com/developer/apis_tech.aspx

Some programming knowledge is required . An Application Programming Interface (API) gives you the possibility to access to the data of a website in a programmatic way. Reuters has a dataset that includes financial news and press releases . All you have to do is to write a few lines of code for accessing this information.

Kaggle.com, a community approach:

• Link: https://www.kaggle.com/datasets

Kaggle offers for its members the possibility to access and share data. In addition, it offers also a set of demo code for accessing and manipulating the data. The community is data science driven. However, some of the datasets do not require particular programming skills.

Get your data by yourself:

• link: https://www.programmableweb.com/

There is an ever growing amount of websites that offer the possibility to access them programmatically. Programmableweb is a directory that tries to list them. In addition, it lists links to the proper resources for using them. Note that use the APIs will require some programming . However, you may end up creating original datasets and this is already a valuable outcome of your thesis.

Where to get data for your thesis? In summary:

I hope that by now you realized that datasets are far from been a sparse resource. This collection or resources can serve you as the starting point for finding what you are looking for or for refining/developing your research idea.

This article (Where to get Data: a collection of resources for your thesis) is part of the miniseries on how to do a good thesis, you can see the full list of posts at the following link:

How to Do a Good Thesis: the Miniseries

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