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Let’s face it, no one wants to make decisions based on guesswork or gut feelings. The most important objective of data collection is to ensure that the data gathered is reliable and packed to the brim with juicy insights that can be analyzed and turned into data-driven decisions. There’s nothing better than good statistical analysis .
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Collecting high-quality data is essential for conducting market research, analyzing user behavior, or just trying to get a handle on business operations. With the right approach and a few handy tools, gathering reliable and informative data.
So, let’s get ready to collect some data because when it comes to data collection, it’s all about the details.
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Data collection methods, data collection examples, reasons to conduct online research and data collection, conducting customer surveys for data collection to multiply sales, steps to effectively conduct an online survey for data collection, survey design for data collection.
Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.
Put simply, data collection is the process of gathering information for a specific purpose. It can be used to answer research questions, make informed business decisions, or improve products and services.
To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, data collection is the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.
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There are many ways to collect information when doing research. The data collection methods that the researcher chooses will depend on the research question posed. Some data collection methods include surveys, interviews, tests, physiological evaluations, observations, reviews of existing records, and biological samples. Let’s explore them.
LEARN ABOUT: Best Data Collection Tools
Essentially there are four choices for data collection – in-person interviews, mail, phone, and online. There are pros and cons to each of these modes.
In-person interviews always are better, but the big drawback is the trap you might fall into if you don’t do them regularly. It is expensive to regularly conduct interviews and not conducting enough interviews might give you false positives. Validating your research is almost as important as designing and conducting it.
We’ve seen many instances where after the research is conducted – if the results do not match up with the “gut-feel” of upper management, it has been dismissed off as anecdotal and a “one-time” phenomenon. To avoid such traps, we strongly recommend that data-collection be done on an “ongoing and regular” basis.
LEARN ABOUT: Research Process Steps
This will help you compare and analyze the change in perceptions according to marketing for your products/services. The other issue here is sample size. To be confident with your research, you must interview enough people to weed out the fringe elements.
A couple of years ago there was a lot of discussion about online surveys and their statistical analysis plan . The fact that not every customer had internet connectivity was one of the main concerns.
LEARN ABOUT: Statistical Analysis Methods
Although some of the discussions are still valid, the reach of the internet as a means of communication has become vital in the majority of customer interactions. According to the US Census Bureau, the number of households with computers has doubled between 1997 and 2001.
Learn more: Quantitative Market Research
In 2001 nearly 50% of households had a computer. Nearly 55% of all households with an income of more than 35,000 have internet access, which jumps to 70% for households with an annual income of 50,000. This data is from the US Census Bureau for 2001.
There are primarily three modes of data collection that can be employed to gather feedback – Mail, Phone, and Online. The method actually used for data collection is really a cost-benefit analysis. There is no slam-dunk solution but you can use the table below to understand the risks and advantages associated with each of the mediums:
Paper | $20 – $30 | Medium | 100% |
Phone | $20 – $35 | High | 95% |
Online / Email | $1 – $5 | Medium | 50-70% |
Keep in mind, the reach here is defined as “All U.S. Households.” In most cases, you need to look at how many of your customers are online and determine. If all your customers have email addresses, you have a 100% reach of your customers.
Another important thing to keep in mind is the ever-increasing dominance of cellular phones over landline phones. United States FCC rules prevent automated dialing and calling cellular phone numbers and there is a noticeable trend towards people having cellular phones as the only voice communication device.
This introduces the inability to reach cellular phone customers who are dropping home phone lines in favor of going entirely wireless. Even if automated dialing is not used, another FCC rule prohibits from phoning anyone who would have to pay for the call.
Learn more: Qualitative Market Research
Surveys, where the data is collected via different modes (online, paper, phone etc.), is also another way of going. It is fairly straightforward and easy to have an online survey and have data-entry operators to enter in data (from the phone as well as paper surveys) into the system. The same system can also be used to collect data directly from the respondents.
Learn more: Survey Research
Data collection is an important aspect of research. Let’s consider an example of a mobile manufacturer, company X, which is launching a new product variant. To conduct research about features, price range, target market, competitor analysis, etc. data has to be collected from appropriate sources.
The marketing team can conduct various data collection activities such as online surveys or focus groups .
The survey should have all the right questions about features and pricing, such as “What are the top 3 features expected from an upcoming product?” or “How much are your likely to spend on this product?” or “Which competitors provide similar products?” etc.
For conducting a focus group, the marketing team should decide the participants and the mediator. The topic of discussion and objective behind conducting a focus group should be clarified beforehand to conduct a conclusive discussion.
Data collection methods are chosen depending on the available resources. For example, conducting questionnaires and surveys would require the least resources, while focus groups require moderately high resources.
Feedback is a vital part of any organization’s growth. Whether you conduct regular focus groups to elicit information from key players or, your account manager calls up all your marquee accounts to find out how things are going – essentially they are all processes to find out from your customers’ eyes – How are we doing? What can we do better?
Online surveys are just another medium to collect feedback from your customers , employees and anyone your business interacts with. With the advent of Do-It-Yourself tools for online surveys, data collection on the internet has become really easy, cheap and effective.
Learn more: Online Research
It is a well-established marketing fact that acquiring a new customer is 10 times more difficult and expensive than retaining an existing one. This is one of the fundamental driving forces behind the extensive adoption and interest in CRM and related customer retention tactics.
In a research study conducted by Rice University Professor Dr. Paul Dholakia and Dr. Vicki Morwitz, published in Harvard Business Review, the experiment inferred that the simple fact of asking customers how an organization was performing by itself to deliver results proved to be an effective customer retention strategy.
In the research study, conducted over the course of a year, one set of customers were sent out a satisfaction and opinion survey and the other set was not surveyed. In the next one year, the group that took the survey saw twice the number of people continuing and renewing their loyalty towards the organization data .
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The research study provided a couple of interesting reasons on the basis of consumer psychology, behind this phenomenon:
Surveys should be considered as a critical tool in the customer journey dialog. The best thing about surveys is its ability to carry “bi-directional” information. The research conducted by Paul Dholakia and Vicki Morwitz shows that surveys not only get you the information that is critical for your business, but also enhances and builds upon the established relationship you have with your customers.
Recent technological advances have made it incredibly easy to conduct real-time surveys and opinion polls . Online tools make it easy to frame questions and answers and create surveys on the Web. Distributing surveys via email, website links or even integration with online CRM tools like Salesforce.com have made online surveying a quick-win solution.
So, you’ve decided to conduct an online survey. There are a few questions in your mind that you would like answered, and you are looking for a fast and inexpensive way to find out more about your customers, clients, etc.
First and foremost thing you need to decide what the smart objectives of the study are. Ensure that you can phrase these objectives as questions or measurements. If you can’t, you are better off looking at other data sources like focus groups and other qualitative methods . The data collected via online surveys is dominantly quantitative in nature.
Review the basic objectives of the study. What are you trying to discover? What actions do you want to take as a result of the survey? – Answers to these questions help in validating collected data. Online surveys are just one way of collecting and quantifying data .
Learn more: Qualitative Data & Qualitative Data Collection Methods
Online surveys have, over the course of time, evolved into an effective alternative to expensive mail or telephone surveys. However, you must be aware of a few conditions that need to be met for online surveys. If you are trying to survey a sample representing the target population, please remember that not everyone is online.
Moreover, not everyone is receptive to an online survey also. Generally, the demographic segmentation of younger individuals is inclined toward responding to an online survey.
Learn More: Examples of Qualitarive Data in Education
Good survey design is crucial for accurate data collection. From question-wording to response options, let’s explore how to create effective surveys that yield valuable insights with our tips to survey design.
Writing great questions can be considered an art. Art always requires a significant amount of hard work, practice, and help from others.
The questions in a survey need to be clear, concise, and unbiased. A poorly worded question or a question with leading language can result in inaccurate or irrelevant responses, ultimately impacting the data’s validity.
Moreover, the questions should be relevant and specific to the research objectives. Questions that are irrelevant or do not capture the necessary information can lead to incomplete or inconsistent responses too.
A small change in content can produce effective results. Words such as could , should and might are all used for almost the same purpose, but may produce a 20% difference in agreement to a question. For example, “The management could.. should.. might.. have shut the factory”.
Intense words such as – prohibit or action, representing control or action, produce similar results. For example, “Do you believe Donald Trump should prohibit insurance companies from raising rates?”.
Sometimes the content is just biased. For instance, “You wouldn’t want to go to Rudolpho’s Restaurant for the organization’s annual party, would you?”
Questions should always reference the intended context, and questions placed out of order or without its requirement should be avoided. Generally, a funnel approach should be implemented – generic questions should be included in the initial section of the questionnaire as a warm-up and specific ones should follow. Toward the end, demographic or geographic questions should be included.
Multiple-choice answers should be mutually unique to provide distinct choices. Overlapping answer options frustrate the respondent and make interpretation difficult at best. Also, the questions should always be precise.
For example: “Do you like water juice?”
This question is vague. In which terms is the liking for orange juice is to be rated? – Sweetness, texture, price, nutrition etc.
Asking about industry-related terms such as caloric content, bits, bytes, MBS , as well as other terms and acronyms can confuse respondents . Ensure that the audience understands your language level, terminology, and, above all, the question you ask.
In survey design for data collection, non-directed questions can give respondents excessive leeway, which can lead to vague and unreliable data. These types of questions are also known as open-ended questions, and they do not provide any structure for the respondent to follow.
For instance, a non-directed question like “ What suggestions do you have for improving our shoes?” can elicit a wide range of answers, some of which may not be relevant to the research objectives. Some respondents may give short answers, while others may provide lengthy and detailed responses, making comparing and analyzing the data challenging.
To avoid these issues, it’s essential to ask direct questions that are specific and have a clear structure. Closed-ended questions, for example, offer structured response options and can be easier to analyze as they provide a quantitative measure of respondents’ opinions.
There will always be certain questions that cross certain privacy rules. Since privacy is an important issue for most people, these questions should either be eliminated from the survey or not be kept as mandatory. Survey questions about income, family income, status, religious and political beliefs, etc., should always be avoided as they are considered to be intruding, and respondents can choose not to answer them.
Unbalanced answer options in scales such as Likert Scale and Semantic Scale may be appropriate for some situations and biased in others. When analyzing a pattern in eating habits, a study used a quantity scale that made obese people appear in the middle of the scale with the polar ends reflecting a state where people starve and an irrational amount to consume. There are cases where we usually do not expect poor service, such as hospitals.
In survey design for data collection, questions that cover two points can be problematic for several reasons. These types of questions are often called “double-barreled” questions and can cause confusion for respondents, leading to inaccurate or irrelevant data.
For instance, a question like “Do you like the food and the service at the restaurant?” covers two points, the food and the service, and it assumes that the respondent has the same opinion about both. If the respondent only liked the food, their opinion of the service could affect their answer.
It’s important to ask one question at a time to avoid confusion and ensure that the respondent’s answer is focused and accurate. This also applies to questions with multiple concepts or ideas. In these cases, it’s best to break down the question into multiple questions that address each concept or idea separately.
Dichotomous questions are used in case you want a distinct answer, such as: Yes/No or Male/Female . For example, the question “Do you think this candidate will win the election?” can be Yes or No.
The use of long questions will definitely increase the time taken for completion, which will generally lead to an increase in the survey dropout rate. Multiple-choice questions are the longest and most complex, and open-ended questions are the shortest and easiest to answer.
Data collection is an essential part of the research process, whether you’re conducting scientific experiments, market research, or surveys. The methods and tools used for data collection will vary depending on the research type, the sample size required, and the resources available.
Several data collection methods include surveys, observations, interviews, and focus groups. We learn each method has advantages and disadvantages, and choosing the one that best suits the research goals is important.
With the rise of technology, many tools are now available to facilitate data collection, including online survey software and data visualization tools. These tools can help researchers collect, store, and analyze data more efficiently, providing greater results and accuracy.
By understanding the various methods and tools available for data collection, we can develop a solid foundation for conducting research. With these research skills , we can make informed decisions, solve problems, and contribute to advancing our understanding of the world around us.
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You will obtain accurate insights with various techniques, including conjoint analysis, MaxDiff analysis, sentiment analysis, TURF analysis, heatmap analysis, etc. Export quality data to external in-depth analysis tools such as SPSS and R Software, and integrate your research with external business applications. Everything you need for your data collection. Start today for free!
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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 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 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 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 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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Methodology
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
Second, decide how you will analyze the data .
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
Qualitative | to broader populations. . | |
---|---|---|
Quantitative | . |
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | . | methods. |
---|---|---|
Secondary |
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
Descriptive | . . | |
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Experimental |
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Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
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.
Research bias
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.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
The research methods you use depend on the type of data you need to answer your research question .
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
Other students also liked, writing strong research questions | criteria & examples.
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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:
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.
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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).
fit K attipoe
Waqar Muhammad
business ethics and leadership
Mary Constantoglou
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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GSI Journals Serie A: Advancements in Tourism, Recreation and Sports Sciences (GSI Dergileri Seri A: Turizm, Rekreasyon ve Spor Bilimlerindeki Gelişmeler)
Mehmet Halit . Akın
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Margarithe charlotte schlunegger.
1 Department of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Bern, Switzerland
2 Faculty of Health, School of Nursing Science, Witten/Herdecke University, Witten, Germany
Rebecca palm.
3 Department of Health Care Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
Supplemental material, sj-docx-1-wjn-10.1177_01939459241263011 for Methodologic and Data-Analysis Triangulation in Case Studies: A Scoping Review by Margarithe Charlotte Schlunegger, Maya Zumstein-Shaha and Rebecca Palm in Western Journal of Nursing Research
We sought to explore the processes of methodologic and data-analysis triangulation in case studies using the example of research on nurse practitioners in primary health care.
We conducted a scoping review within Arksey and O’Malley’s methodological framework, considering studies that defined a case study design and used 2 or more data sources, published in English or German before August 2023.
The databases searched were MEDLINE and CINAHL, supplemented with hand searching of relevant nursing journals. We also examined the reference list of all the included studies.
In total, 63 reports were assessed for eligibility. Ultimately, we included 8 articles. Five studies described within-method triangulation, whereas 3 provided information on between/across-method triangulation. No study reported within-method triangulation of 2 or more quantitative data-collection procedures. The data-collection procedures were interviews, observation, documentation/documents, service records, and questionnaires/assessments. The data-analysis triangulation involved various qualitative and quantitative methods of analysis. Details about comparing or contrasting results from different qualitative and mixed-methods data were lacking.
Various processes for methodologic and data-analysis triangulation are described in this scoping review but lack detail, thus hampering standardization in case study research, potentially affecting research traceability. Triangulation is complicated by terminological confusion. To advance case study research in nursing, authors should reflect critically on the processes of triangulation and employ existing tools, like a protocol or mixed-methods matrix, for transparent reporting. The only existing reporting guideline should be complemented with directions on methodologic and data-analysis triangulation.
Case study research is defined as “an empirical method that investigates a contemporary phenomenon (the ‘case’) in depth and within its real-world context, especially when the boundaries between phenomenon and context may not be clearly evident. A case study relies on multiple sources of evidence, with data needing to converge in a triangulating fashion.” 1 (p15) This design is described as a stand-alone research approach equivalent to grounded theory and can entail single and multiple cases. 1 , 2 However, case study research should not be confused with single clinical case reports. “Case reports are familiar ways of sharing events of intervening with single patients with previously unreported features.” 3 (p107) As a methodology, case study research encompasses substantially more complexity than a typical clinical case report. 1 , 3
A particular characteristic of case study research is the use of various data sources, such as quantitative data originating from questionnaires as well as qualitative data emerging from interviews, observations, or documents. Therefore, a case study always draws on multiple sources of evidence, and the data must converge in a triangulating manner. 1 When using multiple data sources, a case or cases can be examined more convincingly and accurately, compensating for the weaknesses of the respective data sources. 1 Another characteristic is the interaction of various perspectives. This involves comparing or contrasting perspectives of people with different points of view, eg, patients, staff, or leaders. 4 Through triangulation, case studies contribute to the completeness of the research on complex topics, such as role implementation in clinical practice. 1 , 5 Triangulation involves a combination of researchers from various disciplines, of theories, of methods, and/or of data sources. By creating connections between these sources (ie, investigator, theories, methods, data sources, and/or data analysis), a new understanding of the phenomenon under study can be obtained. 6 , 7
This scoping review focuses on methodologic and data-analysis triangulation because concrete procedures are missing, eg, in reporting guidelines. Methodologic triangulation has been called methods, mixed methods, or multimethods. 6 It can encompass within-method triangulation and between/across-method triangulation. 7 “Researchers using within-method triangulation use at least 2 data-collection procedures from the same design approach.” 6 (p254) Within-method triangulation is either qualitative or quantitative but not both. Therefore, within-method triangulation can also be considered data source triangulation. 8 In contrast, “researchers using between/across-method triangulation employ both qualitative and quantitative data-collection methods in the same study.” 6 (p254) Hence, methodologic approaches are combined as well as various data sources. For this scoping review, the term “methodologic triangulation” is maintained to denote between/across-method triangulation. “Data-analysis triangulation is the combination of 2 or more methods of analyzing data.” 6 (p254)
Although much has been published on case studies, there is little consensus on the quality of the various data sources, the most appropriate methods, or the procedures for conducting methodologic and data-analysis triangulation. 5 According to the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) clearinghouse for reporting guidelines, one standard exists for organizational case studies. 9 Organizational case studies provide insights into organizational change in health care services. 9 Rodgers et al 9 pointed out that, although high-quality studies are being funded and published, they are sometimes poorly articulated and methodologically inadequate. In the reporting checklist by Rodgers et al, 9 a description of the data collection is included, but reporting directions on methodologic and data-analysis triangulation are missing. Therefore, the purpose of this study was to examine the process of methodologic and data-analysis triangulation in case studies. Accordingly, we conducted a scoping review to elicit descriptions of and directions for triangulation methods and analysis, drawing on case studies of nurse practitioners (NPs) in primary health care as an example. Case studies are recommended to evaluate the implementation of new roles in (primary) health care, such as that of NPs. 1 , 5 Case studies on new role implementation can generate a unique and in-depth understanding of specific roles (individual), teams (smaller groups), family practices or similar institutions (organization), and social and political processes in health care systems. 1 , 10 The integration of NPs into health care systems is at different stages of progress around the world. 11 Therefore, studies are needed to evaluate this process.
The methodological framework by Arksey and O’Malley 12 guided this scoping review. We examined the current scientific literature on the use of methodologic and data-analysis triangulation in case studies on NPs in primary health care. The review process included the following stages: (1) establishing the research question; (2) identifying relevant studies; (3) selecting the studies for inclusion; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consulting experts in the field. 12 Stage 6 was not performed due to a lack of financial resources. The reporting of the review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review) guideline by Tricco et al 13 (guidelines for reporting systematic reviews and meta-analyses [ Supplementary Table A ]). Scoping reviews are not eligible for registration in PROSPERO.
The aim of this scoping review was to examine the process of triangulating methods and analysis in case studies on NPs in primary health care to improve the reporting. We sought to answer the following question: How have methodologic and data-analysis triangulation been conducted in case studies on NPs in primary health care? To answer the research question, we examined the following elements of the selected studies: the research question, the study design, the case definition, the selected data sources, and the methodologic and data-analysis triangulation.
A systematic database search was performed in the MEDLINE (via PubMed) and CINAHL (via EBSCO) databases between July and September 2020 to identify relevant articles. The following terms were used as keyword search strategies: (“Advanced Practice Nursing” OR “nurse practitioners”) AND (“primary health care” OR “Primary Care Nursing”) AND (“case study” OR “case studies”). Searches were limited to English- and German-language articles. Hand searches were conducted in the journals Nursing Inquiry , BMJ Open , and BioMed Central ( BMC ). We also screened the reference lists of the studies included. The database search was updated in August 2023. The complete search strategy for all the databases is presented in Supplementary Table B .
Inclusion and exclusion criteria.
We used the inclusion and exclusion criteria reported in Table 1 . We included studies of NPs who had at least a master’s degree in nursing according to the definition of the International Council of Nurses. 14 This scoping review considered studies that were conducted in primary health care practices in rural, urban, and suburban regions. We excluded reviews and study protocols in which no data collection had occurred. Articles were included without limitations on the time period or country of origin.
Inclusion and Exclusion Criteria.
Criteria | Inclusion | Exclusion |
---|---|---|
Population | - NPs with a master’s degree in nursing or higher | - Nurses with a bachelor’s degree in nursing or lower - Pre-registration nursing students - No definition of master’s degree in nursing described in the publication |
Interest | - Description/definition of a case study design - Two or more data sources | - Reviews - Study protocols - Summaries/comments/discussions |
Context | - Primary health care - Family practices and home visits (including adult practices, internal medicine practices, community health centers) | - Nursing homes, hospital, hospice |
After the search, we collated and uploaded all the identified records into EndNote v.X8 (Clarivate Analytics, Philadelphia, Pennsylvania) and removed any duplicates. Two independent reviewers (MCS and SA) screened the titles and abstracts for assessment in line with the inclusion criteria. They retrieved and assessed the full texts of the selected studies while applying the inclusion criteria. Any disagreements about the eligibility of studies were resolved by discussion or, if no consensus could be reached, by involving experienced researchers (MZ-S and RP).
The first reviewer (MCS) extracted data from the selected publications. For this purpose, an extraction tool developed by the authors was used. This tool comprised the following criteria: author(s), year of publication, country, research question, design, case definition, data sources, and methodologic and data-analysis triangulation. First, we extracted and summarized information about the case study design. Second, we narratively summarized the way in which the data and methodological triangulation were described. Finally, we summarized the information on within-case or cross-case analysis. This process was performed using Microsoft Excel. One reviewer (MCS) extracted data, whereas another reviewer (SA) cross-checked the data extraction, making suggestions for additions or edits. Any disagreements between the reviewers were resolved through discussion.
A total of 149 records were identified in 2 databases. We removed 20 duplicates and screened 129 reports by title and abstract. A total of 46 reports were assessed for eligibility. Through hand searches, we identified 117 additional records. Of these, we excluded 98 reports after title and abstract screening. A total of 17 reports were assessed for eligibility. From the 2 databases and the hand search, 63 reports were assessed for eligibility. Ultimately, we included 8 articles for data extraction. No further articles were included after the reference list screening of the included studies. A PRISMA flow diagram of the study selection and inclusion process is presented in Figure 1 . As shown in Tables 2 and and3, 3 , the articles included in this scoping review were published between 2010 and 2022 in Canada (n = 3), the United States (n = 2), Australia (n = 2), and Scotland (n = 1).
PRISMA flow diagram.
Characteristics of Articles Included.
Author | Contandriopoulos et al | Flinter | Hogan et al | Hungerford et al | O’Rourke | Roots and MacDonald | Schadewaldt et al | Strachan et al |
---|---|---|---|---|---|---|---|---|
Country | Canada | The United States | The United States | Australia | Canada | Canada | Australia | Scotland |
How or why research question | No information on the research question | Several how or why research questions | What and how research question | No information on the research question | Several how or why research questions | No information on the research question | What research question | What and why research questions |
Design and referenced author of methodological guidance | Six qualitative case studies Robert K. Yin | Multiple-case studies design Robert K. Yin | Multiple-case studies design Robert E. Stake | Case study design Robert K. Yin | Qualitative single-case study Robert K. Yin Robert E. Stake Sharan Merriam | Single-case study design Robert K. Yin Sharan Merriam | Multiple-case studies design Robert K. Yin Robert E. Stake | Multiple-case studies design |
Case definition | Team of health professionals (Small group) | Nurse practitioners (Individuals) | Primary care practices (Organization) | Community-based NP model of practice (Organization) | NP-led practice (Organization) | Primary care practices (Organization) | No information on case definition | Health board (Organization) |
Overview of Within-Method, Between/Across-Method, and Data-Analysis Triangulation.
Author | Contandriopoulos et al | Flinter | Hogan et al | Hungerford et al | O’Rourke | Roots and MacDonald | Schadewaldt et al | Strachan et al |
---|---|---|---|---|---|---|---|---|
Within-method triangulation (using within-method triangulation use at least 2 data-collection procedures from the same design approach) | ||||||||
: | ||||||||
Interviews | X | x | x | x | x | |||
Observations | x | x | ||||||
Public documents | x | x | x | |||||
Electronic health records | x | |||||||
Between/across-method (using both qualitative and quantitative data-collection procedures in the same study) | ||||||||
: | ||||||||
: | ||||||||
Interviews | x | x | x | |||||
Observations | x | x | ||||||
Public documents | x | x | ||||||
Electronic health records | x | |||||||
: | ||||||||
Self-assessment | x | |||||||
Service records | x | |||||||
Questionnaires | x | |||||||
Data-analysis triangulation (combination of 2 or more methods of analyzing data) | ||||||||
: | ||||||||
: | ||||||||
Deductive | x | x | x | |||||
Inductive | x | x | ||||||
Thematic | x | x | ||||||
Content | ||||||||
: | ||||||||
Descriptive analysis | x | x | x | |||||
: | ||||||||
: | ||||||||
Deductive | x | x | x | x | ||||
Inductive | x | x | ||||||
Thematic | x | |||||||
Content | x |
The following sections describe the research question, case definition, and case study design. Case studies are most appropriate when asking “how” or “why” questions. 1 According to Yin, 1 how and why questions are explanatory and lead to the use of case studies, histories, and experiments as the preferred research methods. In 1 study from Canada, eg, the following research question was presented: “How and why did stakeholders participate in the system change process that led to the introduction of the first nurse practitioner-led Clinic in Ontario?” (p7) 19 Once the research question has been formulated, the case should be defined and, subsequently, the case study design chosen. 1 In typical case studies with mixed methods, the 2 types of data are gathered concurrently in a convergent design and the results merged to examine a case and/or compare multiple cases. 10
“How” or “why” questions were found in 4 studies. 16 , 17 , 19 , 22 Two studies additionally asked “what” questions. Three studies described an exploratory approach, and 1 study presented an explanatory approach. Of these 4 studies, 3 studies chose a qualitative approach 17 , 19 , 22 and 1 opted for mixed methods with a convergent design. 16
In the remaining studies, either the research questions were not clearly stated or no “how” or “why” questions were formulated. For example, “what” questions were found in 1 study. 21 No information was provided on exploratory, descriptive, and explanatory approaches. Schadewaldt et al 21 chose mixed methods with a convergent design.
A total of 5 studies defined the case as an organizational unit. 17 , 18 - 20 , 22 Of the 8 articles, 4 reported multiple-case studies. 16 , 17 , 22 , 23 Another 2 publications involved single-case studies. 19 , 20 Moreover, 2 publications did not state the case study design explicitly.
This section describes within-method triangulation, which involves employing at least 2 data-collection procedures within the same design approach. 6 , 7 This can also be called data source triangulation. 8 Next, we present the single data-collection procedures in detail. In 5 studies, information on within-method triangulation was found. 15 , 17 - 19 , 22 Studies describing a quantitative approach and the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review.
Five studies used qualitative data-collection procedures. Two studies combined face-to-face interviews and documents. 15 , 19 One study mixed in-depth interviews with observations, 18 and 1 study combined face-to-face interviews and documentation. 22 One study contained face-to-face interviews, observations, and documentation. 17 The combination of different qualitative data-collection procedures was used to present the case context in an authentic and complex way, to elicit the perspectives of the participants, and to obtain a holistic description and explanation of the cases under study.
All 5 studies used qualitative interviews as the primary data-collection procedure. 15 , 17 - 19 , 22 Face-to-face, in-depth, and semi-structured interviews were conducted. The topics covered in the interviews included processes in the introduction of new care services and experiences of barriers and facilitators to collaborative work in general practices. Two studies did not specify the type of interviews conducted and did not report sample questions. 15 , 18
In 2 studies, qualitative observations were carried out. 17 , 18 During the observations, the physical design of the clinical patients’ rooms and office spaces was examined. 17 Hungerford et al 18 did not explain what information was collected during the observations. In both studies, the type of observation was not specified. Observations were generally recorded as field notes.
In 3 studies, various qualitative public documents were studied. 15 , 19 , 22 These documents included role description, education curriculum, governance frameworks, websites, and newspapers with information about the implementation of the role and general practice. Only 1 study failed to specify the type of document and the collected data. 15
In 1 study, qualitative documentation was investigated. 17 This included a review of dashboards (eg, provider productivity reports or provider quality dashboards in the electronic health record) and quality performance reports (eg, practice-wide or co-management team-wide performance reports).
This section describes the between/across methods, which involve employing both qualitative and quantitative data-collection procedures in the same study. 6 , 7 This procedure can also be denoted “methodologic triangulation.” 8 Subsequently, we present the individual data-collection procedures. In 3 studies, information on between/across triangulation was found. 16 , 20 , 21
Three studies used qualitative and quantitative data-collection procedures. One study combined face-to-face interviews, documentation, and self-assessments. 16 One study employed semi-structured interviews, direct observation, documents, and service records, 20 and another study combined face-to-face interviews, non-participant observation, documents, and questionnaires. 23
All 3 studies used qualitative interviews as the primary data-collection procedure. 16 , 20 , 23 Face-to-face and semi-structured interviews were conducted. In the interviews, data were collected on the introduction of new care services and experiences of barriers to and facilitators of collaborative work in general practices.
In 2 studies, direct and non-participant qualitative observations were conducted. 20 , 23 During the observations, the interaction between health professionals or the organization and the clinical context was observed. Observations were generally recorded as field notes.
In 2 studies, various qualitative public documents were examined. 20 , 23 These documents included role description, newspapers, websites, and practice documents (eg, flyers). In the documents, information on the role implementation and role description of NPs was collected.
In 1 study, qualitative individual journals were studied. 16 These included reflective journals from NPs, who performed the role in primary health care.
Only 1 study involved quantitative service records. 20 These service records were obtained from the primary care practices and the respective health authorities. They were collected before and after the implementation of an NP role to identify changes in patients’ access to health care, the volume of patients served, and patients’ use of acute care services.
In 2 studies, quantitative questionnaires were used to gather information about the teams’ satisfaction with collaboration. 16 , 21 In 1 study, 3 validated scales were used. The scales measured experience, satisfaction, and belief in the benefits of collaboration. 21 Psychometric performance indicators of these scales were provided. However, the time points of data collection were not specified; similarly, whether the questionnaires were completed online or by hand was not mentioned. A competency self-assessment tool was used in another study. 16 The assessment comprised 70 items and included topics such as health promotion, protection, disease prevention and treatment, the NP-patient relationship, the teaching-coaching function, the professional role, managing and negotiating health care delivery systems, monitoring and ensuring the quality of health care practice, and cultural competence. Psychometric performance indicators were provided. The assessment was completed online with 2 measurement time points (pre self-assessment and post self-assessment).
This section describes data-analysis triangulation, which involves the combination of 2 or more methods of analyzing data. 6 Subsequently, we present within-case analysis and cross-case analysis.
Three studies combined qualitative and quantitative methods of analysis. 16 , 20 , 21 Two studies involved deductive and inductive qualitative analysis, and qualitative data were analyzed thematically. 20 , 21 One used deductive qualitative analysis. 16 The method of analysis was not specified in the studies. Quantitative data were analyzed using descriptive statistics in 3 studies. 16 , 20 , 23 The descriptive statistics comprised the calculation of the mean, median, and frequencies.
Two studies combined deductive and inductive qualitative analysis, 19 , 22 and 2 studies only used deductive qualitative analysis. 15 , 18 Qualitative data were analyzed thematically in 1 study, 22 and data were treated with content analysis in the other. 19 The method of analysis was not specified in the 2 studies.
In 7 studies, a within-case analysis was performed. 15 - 20 , 22 Six studies used qualitative data for the within-case analysis, and 1 study employed qualitative and quantitative data. Data were analyzed separately, consecutively, or in parallel. The themes generated from qualitative data were compared and then summarized. The individual cases were presented mostly as a narrative description. Quantitative data were integrated into the qualitative description with tables and graphs. Qualitative and quantitative data were also presented as a narrative description.
Of the multiple-case studies, 5 carried out cross-case analyses. 15 - 17 , 20 , 22 Three studies described the cross-case analysis using qualitative data. Two studies reported a combination of qualitative and quantitative data for the cross-case analysis. In each multiple-case study, the individual cases were contrasted to identify the differences and similarities between the cases. One study did not specify whether a within-case or a cross-case analysis was conducted. 23
This section describes confirmation or contradiction through qualitative and quantitative data. 1 , 4 Qualitative and quantitative data were reported separately, with little connection between them. As a result, the conclusions on neither the comparisons nor the contradictions could be clearly determined.
In 3 studies, the consistency of the results of different types of qualitative data was highlighted. 16 , 19 , 21 In particular, documentation and interviews or interviews and observations were contrasted:
Both types of data showed that NPs and general practitioners wanted to have more time in common to discuss patient cases and engage in personal exchanges. 21 In addition, the qualitative and quantitative data confirmed the individual progression of NPs from less competent to more competent. 16 One study pointed out that qualitative and quantitative data obtained similar results for the cases. 20 For example, integrating NPs improved patient access by increasing appointment availability.
Although questionnaire results indicated that NPs and general practitioners experienced high levels of collaboration and satisfaction with the collaborative relationship, the qualitative results drew a more ambivalent picture of NPs’ and general practitioners’ experiences with collaboration. 21
The studies included in this scoping review evidenced various research questions. The recommended formats (ie, how or why questions) were not applied consistently. Therefore, no case study design should be applied because the research question is the major guide for determining the research design. 2 Furthermore, case definitions and designs were applied variably. The lack of standardization is reflected in differences in the reporting of these case studies. Generally, case study research is viewed as allowing much more freedom and flexibility. 5 , 24 However, this flexibility and the lack of uniform specifications lead to confusion.
Methodologic triangulation, as described in the literature, can be somewhat confusing as it can refer to either data-collection methods or research designs. 6 , 8 For example, methodologic triangulation can allude to qualitative and quantitative methods, indicating a paradigmatic connection. Methodologic triangulation can also point to qualitative and quantitative data-collection methods, analysis, and interpretation without specific philosophical stances. 6 , 8 Regarding “data-collection methods with no philosophical stances,” we would recommend using the wording “data source triangulation” instead. Thus, the demarcation between the method and the data-collection procedures will be clearer.
Yin 1 advocated the use of multiple sources of evidence so that a case or cases can be investigated more comprehensively and accurately. Most studies included multiple data-collection procedures. Five studies employed a variety of qualitative data-collection procedures, and 3 studies used qualitative and quantitative data-collection procedures (mixed methods). In contrast, no study contained 2 or more quantitative data-collection procedures. In particular, quantitative data-collection procedures—such as validated, reliable questionnaires, scales, or assessments—were not used exhaustively. The prerequisites for using multiple data-collection procedures are availability, the knowledge and skill of the researcher, and sufficient financial funds. 1 To meet these prerequisites, research teams consisting of members with different levels of training and experience are necessary. Multidisciplinary research teams need to be aware of the strengths and weaknesses of different data sources and collection procedures. 1
When using multiple data sources and analysis methods, it is necessary to present the results in a coherent manner. Although the importance of multiple data sources and analysis has been emphasized, 1 , 5 the description of triangulation has tended to be brief. Thus, traceability of the research process is not always ensured. The sparse description of the data-analysis triangulation procedure may be due to the limited number of words in publications or the complexity involved in merging the different data sources.
Only a few concrete recommendations regarding the operationalization of the data-analysis triangulation with the qualitative data process were found. 25 A total of 3 approaches have been proposed 25 : (1) the intuitive approach, in which researchers intuitively connect information from different data sources; (2) the procedural approach, in which each comparative or contrasting step in triangulation is documented to ensure transparency and replicability; and (3) the intersubjective approach, which necessitates a group of researchers agreeing on the steps in the triangulation process. For each case study, one of these 3 approaches needs to be selected, carefully carried out, and documented. Thus, in-depth examination of the data can take place. Farmer et al 25 concluded that most researchers take the intuitive approach; therefore, triangulation is not clearly articulated. This trend is also evident in our scoping review.
Few studies in this scoping review used a combination of qualitative and quantitative analysis. However, creating a comprehensive stand-alone picture of a case from both qualitative and quantitative methods is challenging. Findings derived from different data types may not automatically coalesce into a coherent whole. 4 O’Cathain et al 26 described 3 techniques for combining the results of qualitative and quantitative methods: (1) developing a triangulation protocol; (2) following a thread by selecting a theme from 1 component and following it across the other components; and (3) developing a mixed-methods matrix.
The most detailed description of the conducting of triangulation is the triangulation protocol. The triangulation protocol takes place at the interpretation stage of the research process. 26 This protocol was developed for multiple qualitative data but can also be applied to a combination of qualitative and quantitative data. 25 , 26 It is possible to determine agreement, partial agreement, “silence,” or dissonance between the results of qualitative and quantitative data. The protocol is intended to bring together the various themes from the qualitative and quantitative results and identify overarching meta-themes. 25 , 26
The “following a thread” technique is used in the analysis stage of the research process. To begin, each data source is analyzed to identify the most important themes that need further investigation. Subsequently, the research team selects 1 theme from 1 data source and follows it up in the other data source, thereby creating a thread. The individual steps of this technique are not specified. 26 , 27
A mixed-methods matrix is used at the end of the analysis. 26 All the data collected on a defined case are examined together in 1 large matrix, paying attention to cases rather than variables or themes. In a mixed-methods matrix (eg, a table), the rows represent the cases for which both qualitative and quantitative data exist. The columns show the findings for each case. This technique allows the research team to look for congruency, surprises, and paradoxes among the findings as well as patterns across multiple cases. In our review, we identified only one of these 3 approaches in the study by Roots and MacDonald. 20 These authors mentioned that a causal network analysis was performed using a matrix. However, no further details were given, and reference was made to a later publication. We could not find this publication.
Because it focused on the implementation of NPs in primary health care, the setting of this scoping review was narrow. However, triangulation is essential for research in this area. This type of research was found to provide a good basis for understanding methodologic and data-analysis triangulation. Despite the lack of traceability in the description of the data and methodological triangulation, we believe that case studies are an appropriate design for exploring new nursing roles in existing health care systems. This is evidenced by the fact that case study research is widely used in many social science disciplines as well as in professional practice. 1 To strengthen this research method and increase the traceability in the research process, we recommend using the reporting guideline and reporting checklist by Rodgers et al. 9 This reporting checklist needs to be complemented with methodologic and data-analysis triangulation. A procedural approach needs to be followed in which each comparative step of the triangulation is documented. 25 A triangulation protocol or a mixed-methods matrix can be used for this purpose. 26 If there is a word limit in a publication, the triangulation protocol or mixed-methods matrix needs to be identified. A schematic representation of methodologic and data-analysis triangulation in case studies can be found in Figure 2 .
Schematic representation of methodologic and data-analysis triangulation in case studies (own work).
This study suffered from several limitations that must be acknowledged. Given the nature of scoping reviews, we did not analyze the evidence reported in the studies. However, 2 reviewers independently reviewed all the full-text reports with respect to the inclusion criteria. The focus on the primary care setting with NPs (master’s degree) was very narrow, and only a few studies qualified. Thus, possible important methodological aspects that would have contributed to answering the questions were omitted. Studies describing the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review due to the inclusion and exclusion criteria.
Given the various processes described for methodologic and data-analysis triangulation, we can conclude that triangulation in case studies is poorly standardized. Consequently, the traceability of the research process is not always given. Triangulation is complicated by the confusion of terminology. To advance case study research in nursing, we encourage authors to reflect critically on methodologic and data-analysis triangulation and use existing tools, such as the triangulation protocol or mixed-methods matrix and the reporting guideline checklist by Rodgers et al, 9 to ensure more transparent reporting.
Acknowledgments.
The authors thank Simona Aeschlimann for her support during the screening process.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
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Table of Contents
Definition:
Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.
Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:
I. Introduction
II. Research Design
III. Data Collection Methods
IV. Data Analysis Methods
V. Ethical Considerations
VI. Limitations
VII. Conclusion
Types of Research Methodology are as follows:
This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.
This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.
This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.
This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.
This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.
This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.
This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.
This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.
An Example of Research Methodology could be the following:
Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults
Introduction:
The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.
Research Design:
The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.
Participants:
Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.
Intervention :
The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.
Data Collection:
Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.
Data Analysis:
Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.
Ethical Considerations:
This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.
Data Management:
All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.
Limitations:
One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.
Conclusion:
This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.
Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:
Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.
The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.
The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.
Here are some of the applications of research methodology:
Research methodology serves several important purposes, including:
Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:
Research Methodology | Research Methods |
---|---|
Research methodology refers to the philosophical and theoretical frameworks that guide the research process. | refer to the techniques and procedures used to collect and analyze data. |
It is concerned with the underlying principles and assumptions of research. | It is concerned with the practical aspects of research. |
It provides a rationale for why certain research methods are used. | It determines the specific steps that will be taken to conduct research. |
It is broader in scope and involves understanding the overall approach to research. | It is narrower in scope and focuses on specific techniques and tools used in research. |
It is concerned with identifying research questions, defining the research problem, and formulating hypotheses. | It is concerned with collecting data, analyzing data, and interpreting results. |
It is concerned with the validity and reliability of research. | It is concerned with the accuracy and precision of data. |
It is concerned with the ethical considerations of research. | It is concerned with the practical considerations of research. |
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Step 2: Choose your data collection method. Based on the data you want to collect, decide which method is best suited for your research. Experimental research is primarily a quantitative method. Interviews, focus groups, and ethnographies are qualitative methods. Surveys, observations, archival research, and secondary data collection can be ...
PDF | Learn how to choose the best data collection methods and tools for your research project, with examples and tips from ResearchGate experts. | Download and read the full-text PDF.
CHAPTER 3: RESEARCH METHODOLOGY. 3.1 Introduction. As it is indicated in the title, this chapter includes the research methodology of. the dissertation. In more details, in this part the author ...
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. Glossary. Empirical data: unique research that may be quantitative, qualitative, or mixed. Theoretical data: secondary, scholarly sources like books and journal articles that ...
There are actually two kinds of mixing of the six major methods of data collection (Johnson & Turner, 2003). The first is intermethod mixing, which means two or moreof the different methods of data collection are used in a research study. This is seen in the two examples in the previous paragraph.
Before selecting a data collection method, the type of data that is required for the study should be determined (Kabir, 2016). This section aims to provide a summary of possible data types to go through the different data collection methods and sources of data based on these categories. However, we need to understand what data is exactly?
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 ...
Types of Data Collection Methods. Data collection methods are important, because how the information collected is used and what explanations it can generate are determined by the methodology and analytical approach applied by the researcher. 1, 2 Five key data collection methods are presented here, with their strengths and limitations described ...
Revised on 10 October 2022. Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.
Data collection is the process of gathering information from various sources via different research methods and consolidating it into a single database or repository so researchers can use it for further analysis. Data collection aims to provide information that individuals, businesses, and organizations can use to solve problems, track progress, and make decisions.
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.
Data Collection: Key Debates and Methods in Social Research by Wendy Olsen. Call Number: H62 .O4 2012eb. ISBN: 9781847872562. Publication Date: 2011-12-06. SAGE Handbook of Mixed Methods in Social and Behavioral Research by Abbas Tashakkori; Charles B. Teddlie. Call Number: H62 .T244 2016eb. ISBN: 9781412972666. Publication Date: 2010-06-21.
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.
It is important to use a data collection method that will help answer your research question(s). Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies, your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g ...
Topic 2: Data Collection. The choice of data collection method is a critical point in the research process. Quantitative data collection typically involves one or more of the following: Surveys, tests, or questionnaires - administered in groups, one-on-one, by mail, or online; Reviews of records or documents using a rubric; or.
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 ...
The approach to data collection is different for different fields of study, depending on the required information. LEARN ABOUT: Action Research. Data Collection Methods. There are many ways to collect information when doing research. The data collection methods that the researcher chooses will depend on the research question posed. Some data ...
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.
To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). Meta-analysis. Quantitative. To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. Thematic analysis.
ISBN: 9788132105961. Publication Date: 2010. A Gentle Guide to Research Methods Gordon Rugg. Provides an overview of research methods, including research design, data collection methods, statistics, and academic writing. This book also includes a coverage of data collection methods - from interviews to indirect observation to card sorts.
Learn about the concept, types, and issues of data collection methods, with examples and tips from ResearchGate's experts. Download the PDF for free.
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.
Therefore, within-method triangulation can also be considered data source triangulation. 8 In contrast, "researchers using between/across-method triangulation employ both qualitative and quantitative data-collection methods in the same study." 6(p254) Hence, methodologic approaches are combined as well as various data sources. For this ...
Data Collection Methods. Describe the methods used to collect data (e.g., surveys, interviews, observations) ... The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data ...