how to write data collection procedure in research

Data collection in research: Your complete guide

Last updated

31 January 2023

Reviewed by

Cathy Heath

In the late 16th century, Francis Bacon coined the phrase "knowledge is power," which implies that knowledge is a powerful force, like physical strength. In the 21st century, knowledge in the form of data is unquestionably powerful.

But data isn't something you just have - you need to collect it. This means utilizing a data collection process and turning the collected data into knowledge that you can leverage into a successful strategy for your business or organization.

Believe it or not, there's more to data collection than just conducting a Google search. In this complete guide, we shine a spotlight on data collection, outlining what it is, types of data collection methods, common challenges in data collection, data collection techniques, and the steps involved in data collection.

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• What is data collection?

There are two specific data collection techniques: primary and secondary data collection. Primary data collection is the process of gathering data directly from sources. It's often considered the most reliable data collection method, as researchers can collect information directly from respondents.

Secondary data collection is data that has already been collected by someone else and is readily available. This data is usually less expensive and quicker to obtain than primary data.

• What are the different methods of data collection?

There are several data collection methods, which can be either manual or automated. Manual data collection involves collecting data manually, typically with pen and paper, while computerized data collection involves using software to collect data from online sources, such as social media, website data, transaction data, etc. 

Here are the five most popular methods of data collection:

Surveys are a very popular method of data collection that organizations can use to gather information from many people. Researchers can conduct multi-mode surveys that reach respondents in different ways, including in person, by mail, over the phone, or online.

As a method of data collection, surveys have several advantages. For instance, they are relatively quick and easy to administer, you can be flexible in what you ask, and they can be tailored to collect data on various topics or from certain demographics.

However, surveys also have several disadvantages. For instance, they can be expensive to administer, and the results may not represent the population as a whole. Additionally, survey data can be challenging to interpret. It may also be subject to bias if the questions are not well-designed or if the sample of people surveyed is not representative of the population of interest.

Interviews are a common method of collecting data in social science research. You can conduct interviews in person, over the phone, or even via email or online chat.

Interviews are a great way to collect qualitative and quantitative data . Qualitative interviews are likely your best option if you need to collect detailed information about your subjects' experiences or opinions. If you need to collect more generalized data about your subjects' demographics or attitudes, then quantitative interviews may be a better option.

Interviews are relatively quick and very flexible, allowing you to ask follow-up questions and explore topics in more depth. The downside is that interviews can be time-consuming and expensive due to the amount of information to be analyzed. They are also prone to bias, as both the interviewer and the respondent may have certain expectations or preconceptions that may influence the data.

Direct observation

Observation is a direct way of collecting data. It can be structured (with a specific protocol to follow) or unstructured (simply observing without a particular plan).

Organizations and businesses use observation as a data collection method to gather information about their target market, customers, or competition. Businesses can learn about consumer behavior, preferences, and trends by observing people using their products or service.

There are two types of observation: participatory and non-participatory. In participatory observation, the researcher is actively involved in the observed activities. This type of observation is used in ethnographic research , where the researcher wants to understand a group's culture and social norms. Non-participatory observation is when researchers observe from a distance and do not interact with the people or environment they are studying.

There are several advantages to using observation as a data collection method. It can provide insights that may not be apparent through other methods, such as surveys or interviews. Researchers can also observe behavior in a natural setting, which can provide a more accurate picture of what people do and how and why they behave in a certain context.

There are some disadvantages to using observation as a method of data collection. It can be time-consuming, intrusive, and expensive to observe people for extended periods. Observations can also be tainted if the researcher is not careful to avoid personal biases or preconceptions.

Automated data collection

Business applications and websites are increasingly collecting data electronically to improve the user experience or for marketing purposes.

There are a few different ways that organizations can collect data automatically. One way is through cookies, which are small pieces of data stored on a user's computer. They track a user's browsing history and activity on a site, measuring levels of engagement with a business’s products or services, for example.

Another way organizations can collect data automatically is through web beacons. Web beacons are small images embedded on a web page to track a user's activity.

Finally, organizations can also collect data through mobile apps, which can track user location, device information, and app usage. This data can be used to improve the user experience and for marketing purposes.

Automated data collection is a valuable tool for businesses, helping improve the user experience or target marketing efforts. Businesses should aim to be transparent about how they collect and use this data.

Sourcing data through information service providers

Organizations need to be able to collect data from a variety of sources, including social media, weblogs, and sensors. The process to do this and then use the data for action needs to be efficient, targeted, and meaningful.

In the era of big data, organizations are increasingly turning to information service providers (ISPs) and other external data sources to help them collect data to make crucial decisions. 

Information service providers help organizations collect data by offering personalized services that suit the specific needs of the organizations. These services can include data collection, analysis, management, and reporting. By partnering with an ISP, organizations can gain access to the newest technology and tools to help them to gather and manage data more effectively.

There are also several tools and techniques that organizations can use to collect data from external sources, such as web scraping, which collects data from websites, and data mining, which involves using algorithms to extract data from large data sets. 

Organizations can also use APIs (application programming interface) to collect data from external sources. APIs allow organizations to access data stored in another system and share and integrate it into their own systems.

Finally, organizations can also use manual methods to collect data from external sources. This can involve contacting companies or individuals directly to request data, by using the right tools and methods to get the insights they need.

• What are common challenges in data collection?

There are many challenges that researchers face when collecting data. Here are five common examples:

Big data environments

Data collection can be a challenge in big data environments for several reasons. It can be located in different places, such as archives, libraries, or online. The sheer volume of data can also make it difficult to identify the most relevant data sets.

Second, the complexity of data sets can make it challenging to extract the desired information. Third, the distributed nature of big data environments can make it difficult to collect data promptly and efficiently.

Therefore it is important to have a well-designed data collection strategy to consider the specific needs of the organization and what data sets are the most relevant. Alongside this, consideration should be made regarding the tools and resources available to support data collection and protect it from unintended use.

Data bias is a common challenge in data collection. It occurs when data is collected from a sample that is not representative of the population of interest. 

There are different types of data bias, but some common ones include selection bias, self-selection bias, and response bias. Selection bias can occur when the collected data does not represent the population being studied. For example, if a study only includes data from people who volunteer to participate, that data may not represent the general population.

Self-selection bias can also occur when people self-select into a study, such as by taking part only if they think they will benefit from it. Response bias happens when people respond in a way that is not honest or accurate, such as by only answering questions that make them look good. 

These types of data bias present a challenge because they can lead to inaccurate results and conclusions about behaviors, perceptions, and trends. Data bias can be avoided by identifying potential sources or themes of bias and setting guidelines for eliminating them.

Lack of quality assurance processes

One of the biggest challenges in data collection is the lack of quality assurance processes. This can lead to several problems, including incorrect data, missing data, and inconsistencies between data sets.

Quality assurance is important because there are many data sources, and each source may have different levels of quality or corruption. There are also different ways of collecting data, and data quality may vary depending on the method used. 

There are several ways to improve quality assurance in data collection. These include developing clear and consistent goals and guidelines for data collection, implementing quality control measures, using standardized procedures, and employing data validation techniques. By taking these steps, you can ensure that your data is of adequate quality to inform decision-making.

Limited access to data

Another challenge in data collection is limited access to data. This can be due to several reasons, including privacy concerns, the sensitive nature of the data, security concerns, or simply the fact that data is not readily available.

Legal and compliance regulations

Most countries have regulations governing how data can be collected, used, and stored. In some cases, data collected in one country may not be used in another. This means gaining a global perspective can be a challenge. 

For example, if a company is required to comply with the EU General Data Protection Regulation (GDPR), it may not be able to collect data from individuals in the EU without their explicit consent. This can make it difficult to collect data from a target audience.

Legal and compliance regulations can be complex, and it's important to ensure that all data collected is done so in a way that complies with the relevant regulations.

• What are the key steps in the data collection process?

There are five steps involved in the data collection process. They are:

1. Decide what data you want to gather

Have a clear understanding of the questions you are asking, and then consider where the answers might lie and how you might obtain them. This saves time and resources by avoiding the collection of irrelevant data, and helps maintain the quality of your datasets. 

2. Establish a deadline for data collection

Establishing a deadline for data collection helps you avoid collecting too much data, which can be costly and time-consuming to analyze. It also allows you to plan for data analysis and prompt interpretation. Finally, it helps you meet your research goals and objectives and allows you to move forward.

3. Select a data collection approach

The data collection approach you choose will depend on different factors, including the type of data you need, available resources, and the project timeline. For instance, if you need qualitative data, you might choose a focus group or interview methodology. If you need quantitative data , then a survey or observational study may be the most appropriate form of collection.

4. Gather information

When collecting data for your business, identify your business goals first. Once you know what you want to achieve, you can start collecting data to reach those goals. The most important thing is to ensure that the data you collect is reliable and valid. Otherwise, any decisions you make using the data could result in a negative outcome for your business.

5. Examine the information and apply your findings

As a researcher, it's important to examine the data you're collecting and analyzing before you apply your findings. This is because data can be misleading, leading to inaccurate conclusions. Ask yourself whether it is what you are expecting? Is it similar to other datasets you have looked at? 

There are many scientific ways to examine data, but some common methods include:

looking at the distribution of data points

examining the relationships between variables

looking for outliers

By taking the time to examine your data and noticing any patterns, strange or otherwise, you can avoid making mistakes that could invalidate your research.

• How qualitative analysis software streamlines the data collection process

Knowledge derived from data does indeed carry power. However, if you don't convert the knowledge into action, it will remain a resource of unexploited energy and wasted potential.

Luckily, data collection tools enable organizations to streamline their data collection and analysis processes and leverage the derived knowledge to grow their businesses. For instance, qualitative analysis software can be highly advantageous in data collection by streamlining the process, making it more efficient and less time-consuming.

Secondly, qualitative analysis software provides a structure for data collection and analysis, ensuring that data is of high quality. It can also help to uncover patterns and relationships that would otherwise be difficult to discern. Moreover, you can use it to replace more expensive data collection methods, such as focus groups or surveys.

Overall, qualitative analysis software can be valuable for any researcher looking to collect and analyze data. By increasing efficiency, improving data quality, and providing greater insights, qualitative software can help to make the research process much more efficient and effective.

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Home Market Research

Data Collection: What It Is, Methods & Tools + Examples

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 .

LEARN ABOUT: Level of Analysis

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.

Content Index

What is Data Collection?

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.

LEARN ABOUT: Action Research

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

Phone vs. Online vs. In-Person Interviews

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.

• Pros: In-depth and a high degree of confidence in the data
• Cons: Time-consuming, expensive, and can be dismissed as anecdotal
• Pros: Can reach anyone and everyone – no barrier
• Cons: Expensive, data collection errors, lag time
• Pros: High degree of confidence in the data collected, reach almost anyone
• Cons: Expensive, cannot self-administer, need to hire an agency
• Pros: Cheap, can self-administer, very low probability of data errors
• Cons: Not all your customers might have an email address/be on the internet, customers may be wary of divulging information online.

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:

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

Multi-Mode Surveys

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 .

Learn more: Research Design

The research study provided a couple of interesting reasons on the basis of consumer psychology, behind this phenomenon:

• Satisfaction surveys boost the customers’ desire to be coddled and induce positive feelings. This crops from a section of the human psychology that intends to “appreciate” a product or service they already like or prefer. The survey feedback collection method is solely a medium to convey this. The survey is a vehicle to “interact” with the company and reinforces the customer’s commitment to the company.
• Surveys may increase awareness of auxiliary products and services. Surveys can be considered modes of both inbound as well as outbound communication. Surveys are generally considered to be a data collection and analysis source. Most people are unaware of the fact that consumer surveys can also serve as a medium for distributing data. It is important to note a few caveats here.
• In most countries, including the US, “selling under the guise of research” is illegal. b. However, we all know that information is distributed while collecting information. c. Other disclaimers may be included in the survey to ensure users are aware of this fact. For example: “We will collect your opinion and inform you about products and services that have come online in the last year…”
• Induced Judgments:  The entire procedure of asking people for their feedback can prompt them to build an opinion on something they otherwise would not have thought about. This is a very underlying yet powerful argument that can be compared to the “Product Placement” strategy currently used for marketing products in mass media like movies and television shows. One example is the extensive and exclusive use of the “mini-Cooper” in the blockbuster movie “Italian Job.” This strategy is questionable and should be used with great caution.

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

• Visualize all of the relevant information items you would like to have. What will the output survey research report look like? What charts and graphs will be prepared? What information do you need to be assured that action is warranted?
• Assign ranks to each topic (1 and 2) according to their priority, including the most important topics first. Revisit these items again to ensure that the objectives, topics, and information you need are appropriate. Remember, you can’t solve the research problem if you ask the wrong questions.
• How easy or difficult is it for the respondent to provide information on each topic? If it is difficult, is there an alternative medium to gain insights by asking a different question? This is probably the most important step. Online surveys have to be Precise, Clear and Concise. Due to the nature of the internet and the fluctuations involved, if your questions are too difficult to understand, the survey dropout rate will be high.
• Create a sequence for the topics that are unbiased. Make sure that the questions asked first do not bias the results of the next questions. Sometimes providing too much information, or disclosing purpose of the study can create bias. Once you have a series of decided topics, you can have a basic structure of a survey. It is always advisable to add an “Introductory” paragraph before the survey to explain the project objective and what is expected of the respondent. It is also sensible to have a “Thank You” text as well as information about where to find the results of the survey when they are published.
• Page Breaks – The attention span of respondents can be very low when it comes to a long scrolling survey. Add page breaks as wherever possible. Having said that, a single question per page can also hamper response rates as it increases the time to complete the survey as well as increases the chances for dropouts.
• Branching – Create smart and effective surveys with the implementation of branching wherever required. Eliminate the use of text such as, “If you answered No to Q1 then Answer Q4” – this leads to annoyance amongst respondents which result in increase survey dropout rates. Design online surveys using the branching logic so that appropriate questions are automatically routed based on previous responses.
• Write the questions . Initially, write a significant number of survey questions out of which you can use the one which is best suited for the survey. Divide the survey into sections so that respondents do not get confused seeing a long list of questions.
• Sequence the questions so that they are unbiased.
• Repeat all of the steps above to find any major holes. Are the questions really answered? Have someone review it for you.
• Time the length of the survey. A survey should take less than five minutes. At three to four research questions per minute, you are limited to about 15 questions. One open end text question counts for three multiple choice questions. Most online software tools will record the time taken for the respondents to answer questions.
• Include a few open-ended survey questions that support your survey object. This will be a type of feedback survey.
• Send an email to the project survey to your test group and then email the feedback survey afterward.
• This way, you can have your test group provide their opinion about the functionality as well as usability of your project survey by using the feedback survey.
• Make changes to your questionnaire based on the received feedback.
• Send the survey out to all your respondents!

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 for data collection

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.

• Avoid loaded or leading words or questions

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?”

• Misplaced questions

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.

• Mutually non-overlapping response categories

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.

• Avoid the use of confusing/unfamiliar words

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.

• Non-directed questions give respondents excessive leeway

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.

• Never force questions

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

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.

• Questions that cover two points

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

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.

• Avoid the use of long questions

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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A Guide to Data Collection: Methods, Process, and Tools

Whether your field is development economics, international development, the nonprofit sector, or myriad other industries, effective data collection is essential. It informs decision-making and increases your organization’s impact. However, the process of data collection can be complex and challenging. If you’re in the beginning stages of creating a data collection process, this guide is for you. It outlines tested methods, efficient procedures, and effective tools to help you improve your data collection activities and outcomes. At SurveyCTO, we’ve used our years of experience and expertise to build a robust, secure, and scalable mobile data collection platform. It’s trusted by respected institutions like The World Bank, J-PAL, Oxfam, and the Gates Foundation, and it’s changed the way many organizations collect and use data. With this guide, we want to share what we know and help you get ready to take the first step in your data collection journey.

Main takeaways from this guide

• Before starting the data collection process, define your goals and identify data sources, which can be primary (first-hand research) or secondary (existing resources).
• Your data collection method should align with your goals, resources, and the nature of the data needed. Surveys, interviews, observations, focus groups, and forms are common data collection methods. 
• Sampling involves selecting a representative group from a larger population. Choosing the right sampling method to gather representative and relevant data is crucial.
• Crafting effective data collection instruments like surveys and questionnaires is key. Instruments should undergo rigorous testing for reliability and accuracy.
• Data collection is an ongoing, iterative process that demands real-time monitoring and adjustments to ensure high-quality, reliable results.
• After data collection, data should be cleaned to eliminate errors and organized for efficient analysis. The data collection journey further extends into data analysis, where patterns and useful information that can inform decision-making are discovered.
• Common challenges in data collection include data quality and consistency issues, data security concerns, and limitations with offline data collection. Employing robust data validation processes, implementing strong security protocols, and using offline-enabled data collection tools can help overcome these challenges.
• Data collection, entry, and management tools and data analysis, visualization, reporting, and workflow tools can streamline the data collection process, improve data quality, and facilitate data analysis.

What is data collection?

The traditional definition of data collection might lead us to think of gathering information through surveys, observations, or interviews. However, the modern-age definition of data collection extends beyond conducting surveys and observations. It encompasses the systematic gathering and recording of any kind of information through digital or manual methods. Data collection can be as routine as a doctor logging a patient’s information into an electronic medical record system during each clinic visit, or as specific as keeping a record of mosquito nets delivered to a rural household.

Getting started with data collection

Before starting your data collection process, you must clearly understand what you aim to achieve and how you’ll get there. Below are some actionable steps to help you get started.

1. Define your goals

Defining your goals is a crucial first step. Engage relevant stakeholders and team members in an iterative and collaborative process to establish clear goals. It’s important that projects start with the identification of key questions and desired outcomes to ensure you focus your efforts on gathering the right information. 

Start by understanding the purpose of your project– what problem are you trying to solve, or what change do you want to bring about? Think about your project’s potential outcomes and obstacles and try to anticipate what kind of data would be useful in these scenarios. Consider who will be using the data you collect and what data would be the most valuable to them. Think about the long-term effects of your project and how you will measure these over time. Lastly, leverage any historical data from previous projects to help you refine key questions that may have been overlooked previously. 

Once questions and outcomes are established, your data collection goals may still vary based on the context of your work. To demonstrate, let’s use the example of an international organization working on a healthcare project in a remote area.

• If you’re a researcher , your goal will revolve around collecting primary data to answer specific questions. This could involve designing a survey or conducting interviews to collect first-hand data on patient improvement, disease or illness prevalence, and behavior changes (such as an increase in patients seeking healthcare).
• If you’re part of the monitoring and evaluation ( M&E) team , your goal will revolve around measuring the success of your healthcare project. This could involve collecting primary data through surveys or observations and developing a dashboard to display real-time metrics like the number of patients treated, percentage of reduction in incidences of disease,, and average patient wait times. Your focus would be using this data to implement any needed program changes and ensure your project meets its objectives.
• If you’re part of a field team , your goal will center around the efficient and accurate execution of project plans. You might be responsible for using data collection tools to capture pertinent information in different settings, such as in interviews takendirectly from the sample community or over the phone. The data you collect and manage will directly influence the operational efficiency of the project and assist in achieving the project’s overarching objectives.

2. Identify your data sources

The crucial next step in your research process is determining your data source. Essentially, there are two main data types to choose from: primary and secondary.

• Primary data is the information you collect directly from first-hand engagements. It’s gathered specifically for your research and tailored to your research question. Primary data collection methods can range from surveys and interviews to focus groups and observations. Because you design the data collection process, primary data can offer precise, context-specific information directly related to your research objectives. For example, suppose you are investigating the impact of a new education policy. In that case, primary data might be collected through surveys distributed to teachers or interviews with school administrators dealing directly with the policy’s implementation.
• Secondary data, on the other hand, is derived from resources that already exist. This can include information gathered for other research projects, administrative records, historical documents, statistical databases, and more. While not originally collected for your specific study, secondary data can offer valuable insights and background information that complement your primary data. For instance, continuing with the education policy example, secondary data might involve academic articles about similar policies, government reports on education or previous survey data about teachers’ opinions on educational reforms.

While both types of data have their strengths, this guide will predominantly focus on primary data and the methods to collect it. Primary data is often emphasized in research because it provides fresh, first-hand insights that directly address your research questions. Primary data also allows for more control over the data collection process, ensuring data is relevant, accurate, and up-to-date.

However, secondary data can offer critical context, allow for longitudinal analysis, save time and resources, and provide a comparative framework for interpreting your primary data. It can be a crucial backdrop against which your primary data can be understood and analyzed. While we focus on primary data collection methods in this guide, we encourage you not to overlook the value of incorporating secondary data into your research design where appropriate.

3. Choose your data collection method

When choosing your data collection method, there are many options at your disposal. Data collection is not limited to methods like surveys and interviews. In fact, many of the processes in our daily lives serve the goal of collecting data, from intake forms to automated endpoints, such as payment terminals and mass transit card readers. Let us dive into some common types of data collection methods: 

Surveys and Questionnaires

Surveys and questionnaires are tools for gathering information about a group of individuals, typically by asking them predefined questions. They can be used to collect quantitative and qualitative data and be administered in various ways, including online, over the phone, in person (offline), or by mail.

• Advantages : They allow researchers to reach many participants quickly and cost-effectively, making them ideal for large-scale studies. The structured format of questions makes analysis easier.
• Disadvantages : They may not capture complex or nuanced information as participants are limited to predefined response choices. Also, there can be issues with response bias, where participants might provide socially desirable answers rather than honest ones.

Interviews involve a one-on-one conversation between the researcher and the participant. The interviewer asks open-ended questions to gain detailed information about the participant’s thoughts, feelings, experiences, and behaviors.

• Advantages : They allow for an in-depth understanding of the topic at hand. The researcher can adapt the questioning in real time based on the participant’s responses, allowing for more flexibility.
• Disadvantages : They can be time-consuming and resource-intensive, as they require trained interviewers and a significant amount of time for both conducting and analyzing responses. They may also introduce interviewer bias if not conducted carefully, due to how an interviewer presents questions and perceives the respondent, and how the respondent perceives the interviewer. 

Observations

Observations involve directly observing and recording behavior or other phenomena as they occur in their natural settings.

• Advantages : Observations can provide valuable contextual information, as researchers can study behavior in the environment where it naturally occurs, reducing the risk of artificiality associated with laboratory settings or self-reported measures.
• Disadvantages : Observational studies may suffer from observer bias, where the observer’s expectations or biases could influence their interpretation of the data. Also, some behaviors might be altered if subjects are aware they are being observed.

Focus Groups

Focus groups are guided discussions among selected individuals to gain information about their views and experiences.

• Advantages : Focus groups allow for interaction among participants, which can generate a diverse range of opinions and ideas. They are good for exploring new topics where there is little pre-existing knowledge.
• Disadvantages : Dominant voices in the group can sway the discussion, potentially silencing less assertive participants. They also require skilled facilitators to moderate the discussion effectively.

Forms are standardized documents with blank fields for collecting data in a systematic manner. They are often used in fields like Customer Relationship Management (CRM) or Electronic Medical Records (EMR) data entry. Surveys may also be referred to as forms.

• Advantages : Forms are versatile, easy to use, and efficient for data collection. They can streamline workflows by standardizing the data entry process.
• Disadvantages : They may not provide in-depth insights as the responses are typically structured and limited. There is also potential for errors in data entry, especially when done manually.

Selecting the right data collection method should be an intentional process, taking into consideration the unique requirements of your project. The method selected should align with your goals, available resources, and the nature of the data you need to collect.

If you aim to collect quantitative data, surveys, questionnaires, and forms can be excellent tools, particularly for large-scale studies. These methods are suited to providing structured responses that can be analyzed statistically, delivering solid numerical data.

However, if you’re looking to uncover a deeper understanding of a subject, qualitative data might be more suitable. In such cases, interviews, observations, and focus groups can provide richer, more nuanced insights. These methods allow you to explore experiences, opinions, and behaviors deeply. Some surveys can also include open-ended questions that provide qualitative data.

The cost of data collection is also an important consideration. If you have budget constraints, in-depth, in-person conversations with every member of your target population may not be practical. In such cases, distributing questionnaires or forms can be a cost-saving approach.

Additional considerations include language barriers and connectivity issues. If your respondents speak different languages, consider translation services or multilingual data collection tools . If your target population resides in areas with limited connectivity and your method will be to collect data using mobile devices, ensure your tool provides offline data collection , which will allow you to carry out your data collection plan without internet connectivity.

4. Determine your sampling method

Now that you’ve established your data collection goals and how you’ll collect your data, the next step is deciding whom to collect your data from. Sampling involves carefully selecting a representative group from a larger population. Choosing the right sampling method is crucial for gathering representative and relevant data that aligns with your data collection goal.

Consider the following guidelines to choose the appropriate sampling method for your research goal and data collection method:

• Understand Your Target Population: Start by conducting thorough research of your target population. Understand who they are, their characteristics, and subgroups within the population.
• Anticipate and Minimize Biases: Anticipate and address potential biases within the target population to help minimize their impact on the data. For example, will your sampling method accurately reflect all ages, gender, cultures, etc., of your target population? Are there barriers to participation for any subgroups? Your sampling method should allow you to capture the most accurate representation of your target population.
• Maintain Cost-Effective Practices: Consider the cost implications of your chosen sampling methods. Some sampling methods will require more resources, time, and effort. Your chosen sampling method should balance the cost factors with the ability to collect your data effectively and accurately. 
• Consider Your Project’s Objectives: Tailor the sampling method to meet your specific objectives and constraints, such as M&E teams requiring real-time impact data and researchers needing representative samples for statistical analysis.

By adhering to these guidelines, you can make informed choices when selecting a sampling method, maximizing the quality and relevance of your data collection efforts.

5. Identify and train collectors

Not every data collection use case requires data collectors, but training individuals responsible for data collection becomes crucial in scenarios involving field presence.

The SurveyCTO platform supports both self-response survey modes and surveys that require a human field worker to do in-person interviews. Whether you’re hiring and training data collectors, utilizing an existing team, or training existing field staff, we offer comprehensive guidance and the right tools to ensure effective data collection practices.  

Here are some common training approaches for data collectors:

• In-Class Training: Comprehensive sessions covering protocols, survey instruments, and best practices empower data collectors with skills and knowledge.
• Tests and Assessments: Assessments evaluate collectors’ understanding and competence, highlighting areas where additional support is needed.
• Mock Interviews: Simulated interviews refine collectors’ techniques and communication skills.
• Pre-Recorded Training Sessions: Accessible reinforcement and self-paced learning to refresh and stay updated.

Training data collectors is vital for successful data collection techniques. Your training should focus on proper instrument usage and effective interaction with respondents, including communication skills, cultural literacy, and ethical considerations.

Remember, training is an ongoing process. Knowledge gaps and issues may arise in the field, necessitating further training.

Moving Ahead: Iterative Steps in Data Collection

Once you’ve established the preliminary elements of your data collection process, you’re ready to start your data collection journey. In this section, we’ll delve into the specifics of designing and testing your instruments, collecting data, and organizing data while embracing the iterative nature of the data collection process, which requires diligent monitoring and making adjustments when needed.

6. Design and test your instruments

Designing effective data collection instruments like surveys and questionnaires is key. It’s crucial to prioritize respondent consent and privacy to ensure the integrity of your research. Thoughtful design and careful testing of survey questions are essential for optimizing research insights. Other critical considerations are: 

• Clear and Unbiased Question Wording: Craft unambiguous, neutral questions free from bias to gather accurate and meaningful data. For example, instead of asking, “Shouldn’t we invest more into renewable energy that will combat the effects of climate change?” ask your question in a neutral way that allows the respondent to voice their thoughts. For example: “What are your thoughts on investing more in renewable energy?”
• Logical Ordering and Appropriate Response Format: Arrange questions logically and choose response formats (such as multiple-choice, Likert scale, or open-ended) that suit the nature of the data you aim to collect.
• Coverage of Relevant Topics: Ensure that your instrument covers all topics pertinent to your data collection goals while respecting cultural and social sensitivities. Make sure your instrument avoids assumptions, stereotypes, and languages or topics that could be considered offensive or taboo in certain contexts. The goal is to avoid marginalizing or offending respondents based on their social or cultural background.
• Collect Only Necessary Data: Design survey instruments that focus solely on gathering the data required for your research objectives, avoiding unnecessary information.
• Language(s) of the Respondent Population: Tailor your instruments to accommodate the languages your target respondents speak, offering translated versions if needed. Similarly, take into account accessibility for respondents who can’t read by offering alternative formats like images in place of text.
• Desired Length of Time for Completion: Respect respondents’ time by designing instruments that can be completed within a reasonable timeframe, balancing thoroughness with engagement. Having a general timeframe for the amount of time needed to complete a response will also help you weed out bad responses. For example, a response that was rushed and completed outside of your response timeframe could indicate a response that needs to be excluded.
• Collecting and Documenting Respondents’ Consent and Privacy: Ensure a robust consent process, transparent data usage communication, and privacy protection throughout data collection.

Perform Cognitive Interviewing

Cognitive interviewing is a method used to refine survey instruments and improve the accuracy of survey responses by evaluating how respondents understand, process, and respond to the instrument’s questions. In practice, cognitive interviewing involves an interview with the respondent, asking them to verbalize their thoughts as they interact with the instrument. By actively probing and observing their responses, you can identify and address ambiguities, ensuring accurate data collection.  

Thoughtful question wording, well-organized response options, and logical sequencing enhance comprehension, minimize biases, and ensure accurate data collection. Iterative testing and refinement based on respondent feedback improve the validity, reliability, and actionability of insights obtained.

Put Your Instrument to the Test

Through rigorous testing, you can uncover flaws, ensure reliability, maximize accuracy, and validate your instrument’s performance. This can be achieved by:

• Conducting pilot testing to enhance the reliability and effectiveness of data collection. Administer the instrument, identify difficulties, gather feedback, and assess performance in real-world conditions.
• Making revisions based on pilot testing to enhance clarity, accuracy, usability, and participant satisfaction. Refine questions, instructions, and format for effective data collection.
• Continuously iterating and refining your instrument based on feedback and real-world testing. This ensures reliable, accurate, and audience-aligned methods of data collection. Additionally, this ensures your instrument adapts to changes, incorporates insights, and maintains ongoing effectiveness.

7. Collect your data

Now that you have your well-designed survey, interview questions, observation plan, or form, it’s time to implement it and gather the needed data. Data collection is not a one-and-done deal; it’s an ongoing process that demands attention to detail. Imagine spending weeks collecting data, only to discover later that a significant portion is unusable due to incomplete responses, improper collection methods, or falsified responses. To avoid such setbacks, adopt an iterative approach.

Leverage data collection tools with real-time monitoring to proactively identify outliers and issues. Take immediate action by fine-tuning your instruments, optimizing the data collection process, addressing concerns like additional training, or reevaluating personnel responsible for inaccurate data (for example, a field worker who sits in a coffee shop entering fake responses rather than doing the work of knocking on doors).

SurveyCTO’s Data Explorer was specifically designed to fulfill this requirement, empowering you to monitor incoming data, gain valuable insights, and know where changes may be needed. Embracing this iterative approach ensures ongoing improvement in data collection, resulting in more reliable and precise results.

8. Clean and organize your data

After data collection, the next step is to clean and organize the data to ensure its integrity and usability.

• Data Cleaning: This stage involves sifting through your data to identify and rectify any errors, inconsistencies, or missing values. It’s essential to maintain the accuracy of your data and ensure that it’s reliable for further analysis. Data cleaning can uncover duplicates, outliers, and gaps that could skew your results if left unchecked. With real-time data monitoring , this continuous cleaning process keeps your data precise and current throughout the data collection period. Similarly, review and corrections workflows allow you to monitor the quality of your incoming data.
• Organizing Your Data: Post-cleaning, it’s time to organize your data for efficient analysis and interpretation. Labeling your data using appropriate codes or categorizations can simplify navigation and streamline the extraction of insights. When you use a survey or form, labeling your data is often not necessary because you can design the instrument to collect in the right categories or return the right codes. An organized dataset is easier to manage, analyze, and interpret, ensuring that your collection efforts are not wasted but lead to valuable, actionable insights.

Remember, each stage of the data collection process, from design to cleaning, is iterative and interconnected. By diligently cleaning and organizing your data, you are setting the stage for robust, meaningful analysis that can inform your data-driven decisions and actions.

What happens after data collection?

The data collection journey takes us next into data analysis, where you’ll uncover patterns, empowering informed decision-making for researchers, evaluation teams, and field personnel.

Process and Analyze Your Data

Explore data through statistical and qualitative techniques to discover patterns, correlations, and insights during this pivotal stage. It’s about extracting the essence of your data and translating numbers into knowledge. Whether applying descriptive statistics, conducting regression analysis, or using thematic coding for qualitative data, this process drives decision-making and charts the path toward actionable outcomes.

Interpret and Report Your Results

Interpreting and reporting your data brings meaning and context to the numbers. Translating raw data into digestible insights for informed decision-making and effective stakeholder communication is critical.

The approach to interpretation and reporting varies depending on the perspective and role:

• Researchers often lean heavily on statistical methods to identify trends, extract meaningful conclusions, and share their findings in academic circles, contributing to their knowledge pool.
• M&E teams typically produce comprehensive reports, shedding light on the effectiveness and impact of programs. These reports guide internal and sometimes external stakeholders, supporting informed decisions and driving program improvements.

Field teams provide a first-hand perspective. Since they are often the first to see the results of the practical implementation of data, field teams are instrumental in providing immediate feedback loops on project initiatives. Field teams do the work that provides context to help research and M&E teams understand external factors like the local environment, cultural nuances, and logistical challenges that impact data results.

Safely store and handle data

Throughout the data collection process, and after it has been collected, it is vital to follow best practices for storing and handling data to ensure the integrity of your research. While the specifics of how to best store and handle data will depend on your project, here are some important guidelines to keep in mind:

• Use cloud storage to hold your data if possible, since this is safer than storing data on hard drives and keeps it more accessible,
• Periodically back up and purge old data from your system, since it’s safer to not retain data longer than necessary,
• If you use mobile devices to collect and store data, use options for private, internal apps-specific storage if and when possible,
• Restrict access to stored data to only those who need to work with that data.

Further considerations for data safety are discussed below in the section on data security .

Remember to uphold ethical standards in interpreting and reporting your data, regardless of your role. Clear communication, respectful handling of sensitive information, and adhering to confidentiality and privacy rights are all essential to fostering trust, promoting transparency, and bolstering your work’s credibility.

Common Data Collection Challenges

Data collection is vital to data-driven initiatives, but it comes with challenges. Addressing common challenges such as poor data quality, privacy concerns, inadequate sample sizes, and bias is essential to ensure the collected data is reliable, trustworthy, and secure. 

In this section, we’ll explore three major challenges: data quality and consistency issues, data security concerns, and limitations with offline data collection , along with strategies to overcome them.

Data Quality and Consistency

Data quality and consistency refer to data accuracy and reliability throughout the collection and analysis process. 

Challenges such as incomplete or missing data, data entry errors, measurement errors, and data coding/categorization errors can impact the integrity and usefulness of the data. 

To navigate these complexities and maintain high standards, consistency, and integrity in the dataset:

• Implement robust data validation processes, 
• Ensure proper training for data entry personnel, 
• Employ automated data validation techniques, and 
• Conduct regular data quality audits.

Data security

Data security encompasses safeguarding data through ensuring data privacy and confidentiality, securing storage and backup, and controlling data sharing and access.

Challenges include the risk of potential breaches, unauthorized access, and the need to comply with data protection regulations.

To address these setbacks and maintain privacy, trust, and confidence during the data collection process: 

• Use encryption and authentication methods, 
• Implement robust security protocols, 
• Update security measures regularly, 
• Provide employee training on data security, and 
• Adopt secure cloud storage solutions.

Offline Data Collection

Offline data collection refers to the process of gathering data using modes like mobile device-based computer-assisted personal interviewing (CAPI) when t here is an inconsistent or unreliable internet connection, and the data collection tool being used for CAPI has the functionality to work offline. 

Challenges associated with offline data collection include synchronization issues, difficulty transferring data, and compatibility problems between devices, and data collection tools. 

To overcome these challenges and enable efficient and reliable offline data collection processes, employ the following strategies: 

• Leverage offline-enabled data collection apps or tools  that enable you to survey respondents even when there’s no internet connection, and upload data to a central repository at a later time. 
• Your data collection plan should include times for periodic data synchronization when connectivity is available, 
• Use offline, device-based storage for seamless data transfer and compatibility, and 
• Provide clear instructions to field personnel on handling offline data collection scenarios.

Utilizing Technology in Data Collection

Embracing technology throughout your data collection process can help you overcome many challenges described in the previous section. Data collection tools can streamline your data collection, improve the quality and security of your data, and facilitate the analysis of your data. Let’s look at two broad categories of tools that are essential for data collection:

Data Collection, Entry, & Management Tools

These tools help with data collection, input, and organization. They can range from digital survey platforms to comprehensive database systems, allowing you to gather, enter, and manage your data effectively. They can significantly simplify the data collection process, minimize human error, and offer practical ways to organize and manage large volumes of data. Some of these tools are:

• Microsoft Office
• Google Docs
• SurveyMonkey
• Google Forms

Data Analysis, Visualization, Reporting, & Workflow Tools

These tools assist in processing and interpreting the collected data. They provide a way to visualize data in a user-friendly format, making it easier to identify trends and patterns. These tools can also generate comprehensive reports to share your findings with stakeholders and help manage your workflow efficiently. By automating complex tasks, they can help ensure accuracy and save time. Tools for these purposes include:

• Google sheets

Data collection tools like SurveyCTO often have integrations to help users seamlessly transition from data collection to data analysis, visualization, reporting, and managing workflows.

Master Your Data Collection Process With SurveyCTO

As we bring this guide to a close, you now possess a wealth of knowledge to develop your data collection process. From understanding the significance of setting clear goals to the crucial process of selecting your data collection methods and addressing common challenges, you are equipped to handle the intricate details of this dynamic process.

Remember, you’re not venturing into this complex process alone. At SurveyCTO, we offer not just a tool but an entire support system committed to your success. Beyond troubleshooting support, our success team serves as research advisors and expert partners, ready to provide guidance at every stage of your data collection journey.

With SurveyCTO , you can design flexible surveys in Microsoft Excel or Google Sheets, collect data online and offline with above-industry-standard security, monitor your data in real time, and effortlessly export it for further analysis in any tool of your choice. You also get access to our Data Explorer, which allows you to visualize incoming data at both individual survey and aggregate levels instantly.

In the iterative data collection process, our users tell us that SurveyCTO stands out with its capacity to establish review and correction workflows. It enables you to monitor incoming data and configure automated quality checks to flag error-prone submissions.

Finally, data security is of paramount importance to us. We ensure best-in-class security measures like SOC 2 compliance, end-to-end encryption, single sign-on (SSO), GDPR-compliant setups, customizable user roles, and self-hosting options to keep your data safe.

As you embark on your data collection journey, you can count on SurveyCTO’s experience and expertise to be by your side every step of the way. Our team would be excited and honored to be a part of your research project, offering you the tools and processes to gain informative insights and make effective decisions. Partner with us today and revolutionize the way you collect data.

Better data, better decision making, better world.

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

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

Published on 4 May 2022 by Pritha Bhandari .

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

Operationalisation

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

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

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

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

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

Standardising procedures

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

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

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

Creating a data management plan

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

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

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

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

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

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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Scientific Research and Methodology : An introduction to quantitative research and statistics

10 collecting data.

So far, you have learnt to ask a RQ and design the study. In this chapter , you will learn how to:

• record the important steps in data collection.
• describe study protocols.
• ask survey questions.

10.1 Protocols

If the RQ is well-constructed, terms are clearly defined, and the study is well designed and explained, then the process for collecting the data should be easy to describe. Data collection is often time-consuming, tedious and expensive, so collecting the data correctly first time is important.

Before collecting the data, a plan should be established and documented that explains exactly how the data will be obtained, which will include operational definitions (Sect. 2.10 ). This plan is called a protocol .

Definition 10.1 (Protocol) A protocol is a procedure documenting the details of the design and implementation of studies, and for data collection.

Unforeseen complications are not unusual, so often a pilot study (or a practice run ) is conducted before the real data collection, to:

• determine the feasibility of the data collection protocol.
• identify unforeseen challenges.
• obtain data to determine appropriate sample sizes (Sect. 30 ).
• potentially save time and money.

The pilot study may suggest changes to the protocol.

Definition 10.2 (Pilot study) A pilot study is a small test run of the study protocol used to check that the protocol is appropriate and practical, and to identify (and hence fix) possible problems with the research design or protocol.

A pilot study allows the researcher

The data can be collected once the protocol has been finalised. Protocols ensure studies are repeatable (Sect. 4.3 ) so others can confirm or compare results, and others can understand exactly what was done, and how. Protocols should indicate how design aspects (such as blinding the individuals, random allocation of treatments, etc.) will happen. The final protocol , without pedantic detail, should be reported. Diagrams can be useful to support explanations. All studies should have a well-established protocol for describing how the study was done.

A protocol usually has at least three components that describe:

• how individuals are chosen from the population (i.e., external validity).
• how information is collected from the individuals (i.e., internal validity).
• the analyses and software (including version) used.

Example 10.1 (Protocol) Romanchik-Cerpovicz, Jeffords, and Onyenwoke ( 2018 ) made cookies using pureed green peas in place of margarine (to increase the nutritional value of cookies). They assessed the acceptance of these cookies to college students.

The protocol discussed how the individuals were chosen (p. 4):

...through advertisement across campus from students attending a university in the southeastern United States.

This voluntary sample comprised \(80.6\) % women, a higher percentage of women than in the general population, or the college population. (Other extraneous variables were also recorded.)

Exclusion criteria were also applied, excluding people "with an allergy or sensitivity to an ingredient used in the preparation of the cookies" (p. 5). The researchers also described how the data was obtained (p. 5):

During the testing session, panelists were seated at individual tables. Each cookie was presented one at a time on a disposable white plate. Samples were previously coded and randomized. The presentation order for all samples was \(25\) %, \(0\) %, \(50\) %, \(100\) % and \(75\) % substitution of fat with puree of canned green peas. To maintain standard procedures for sensory analysis [...], panelists cleansed their palates between cookie samples with distilled water ( \(25^\circ\) C) [...] characteristics of color, smell, moistness, flavor, aftertaste, and overall acceptability, for each sample of cookies [was recorded]...

Thus, internal validity was managed using random allocation, blinding individuals, and washouts. Details are also given of how the cookies were prepared, and how objective measurements (such as moisture content) were determined.

The analyses and software used were also given.

Consider this partial protocol, which shows honesty in describing a protocol:

Fresh cow dung was obtained from free-ranging, grass fed, and antibiotic-free Milking Shorthorn cows ( Bos taurus ) in the Tilden Regional Park in Berkeley, CA. Resting cows were approached with caution and startled by loud shouting, whereupon the cows rapidly stood up, defecated, and moved away from the source of the annoyance. Dung was collected in ZipLoc bags ( \(1\) gallon), snap-frozen and stored at \(-80\)  C. --- Hare et al. ( 2008 ) , p. 10

10.2 Collecting data using questionnaires

10.2.1 writing questions.

Collecting data using questionnaires is common for both observational and experimental studies. Questionnaires are very difficult to do well: question wording is crucial, and surprisingly difficult to get right ( Fink 1995 ) . Pilot testing questionnaires is crucial!

Definition 10.3 (Questionnaire) A questionnaire is a set of questions for respondents to answer.

A questionnaire is a set of question to obtain information from individuals. A survey is an entire methodology, that includes gathering data using a questionnaire, finding a sample, and other components.

Questions in a questionnaire may be open-ended (respondents can write their own answers) or closed (respondents select from a small number of possible answers, as in multiple-choice questions). Open and closed questions both have advantages and disadvantages. Answers to open questions more easily lend themselves to qualitative analysis. This section briefly discusses writing questions.

Example 10.2 (Open and closed questions) Raab and Bogner ( 2021 ) asked German students a series of questions about microplastics, including:

• Name sources of microplastics in the household.
• In which ecosystems are microplastics in Germany? Tick the answer (multiple ticks are possible). Options : (a) sea; (b) rivers; (c) lakes; (d) groundwater.
• Assess the potential danger posed by microplastics. Options : (a) very dangerous; (b) dangerous; (c) hardly dangerous; (d) not dangerous.

The first question is an open : respondents could provide their own answers. The second question is closed , where multiple options can be selected. The third question is closed , where only one option can be selected

Important advice for writing questionnaire questions include:

• Avoid leading questions , which may lead respondents to answer a certain way. Imprecise question wording is the usual reason for leading questions.
• Avoid ambiguity : avoid unfamiliar terms and unclear questions.
• Avoid asking the uninformed : avoid asking respondents about issues they don't know about. Many people will give a response even if they do not understand (such responses are worthless). For example, people may give directions to places that do not even exist ( Collett and O’Shea 1976 ) .
• Avoid complex and double-barrelled questions , which are hard to understand.
• Avoid problems with ethics : avoid questions about people breaking laws, or revealing confidential or private information. In special cases and with justification, ethics committees may allow such questions.
• Ensure clarity in question wording.
• Ensure options are mutually exhaustive , so answers fit into only one category.
• Ensure options are exhaustive , so that the categories cover all options.

Example 10.3 (Poor question wording) Consider a questionnaire asking these questions:

• Because bottles from bottled water create enormous amounts of non-biodegradable landfill and hence threaten native wildlife, do you support banning bottled water?
• Do you drink more water now?
• Are you more concerned about Coagulase-negative Staphylococcus or Neisseria pharyngis in bottled water?
• Do you drink water in plastic and glass bottles?
• Do you have a water tank installed illegally, without permission?
• Do you avoid purchasing water in plastic bottles unless it is carbonated, unless the bottles are plastic but not necessarily if the lid is recyclable?

Question 1 is leading because the expected response is obvious.

Question 2 is ambiguous : it is unclear what 'more water now' is being compared to.

Question 3 is unlikely to give sensible answers, as most people will be uninformed . Many people will still give an opinion, but the data will be effectively useless (though the researcher may not realise).

Question 4 is double-barrelled , and would be better asked as two separate questions (one asking about plastic bottles, and one about glass bottles).

Question 5 is unlikely to be given ethical approval or to obtain truthful answers, as respondents are unlikely to admit to breaking rules.

Question 6 is unclear , since knowing what a yes or no answer means is confusing.

Example 10.4 (Question wording) Question wording can be important. In the 2014 General Social Survey ( https://gss.norc.org ), when white Americans were asked for their opinion of the amount America spends on welfare , \(58\) % of respondents answered 'Too much' ( Jardina 2018 ) .

However, when white Americans were asked for their opinion of the amount America spends on assistance to the poor , only \(16\) % of respondents answered 'Too much'.

Example 10.5 (Leading question) Consider this question:

Do you like this new orthotic?

This question is leading , since liking is the only option presented. Better would be:

Do you like or dislike this new orthotic?

Example 10.6 (Mutually exclusive options) In a study to determine the time doctors spent on patients (from Chan et al. ( 2008 ) ), doctors were given the options:

• \(0\) -- \(5\)  mins;
• \(5\) -- \(10\)  mins; or
• more than \(10\)  mins.

This is a poor question, because a respondent does not know which option to select for an answer of ' \(5\)  minutes'. The options are not mutually exclusive .

The following (humourous) video shows how questions can be manipulated by those not wanting to be ethical:

10.2.2 Challenges using questionnaires

Using questionnaires presents myriad challenges.

• Non-response bias (Sect. 5.11 ): Non-response bias is common with questionnaires, as they are often used with voluntary-response samples. The people who do not respond to the survey may be different than those who do respond.
• Response bias (Sect. 5.11 ): People do not always answer truthfully; for example, what people say may not correspond with what people do (Example 9.6 ). Sometimes this is unintentional (e.g., poor questions wording), due to embarrassment or because questions are controversial. Sometimes, respondents repeatedly provide the same answer to a series of multichoice questions.
• Recall bias : People may not be able to accurately recall past events clearly, or recall when they happened.
• Question order : The order of the questions can influence the responses.
• Interpretation : Phrases and words such as 'Sometimes' and 'Somewhat disagree' may mean different things to different people.

Many of these can be managed with careful questionnaire design, but discussing the methods are beyond the scope of this book.

10.3 Chapter summary

Having a detailed procedure for collecting the data (the protocol ) is important. Using a pilot study to trial the protocol an often reveal unexpected changes necessary for a good protocol. Creating good questionnaires questions is difficult, but important.

10.4 Quick review questions

What is the biggest problem with this question: 'Do you have bromodosis?'

What is the biggest problem with this question: 'Do you spend too much time connected to the internet?'

What is the biggest problem with this question: 'Do you eat fruits and vegetables?'

Which of these are reasons for producing a well-defined protocol?

• It allows the researchers to make the study externally valid. TRUE FALSE
• It ensures that others know exactly what was done. TRUE FALSE
• It ensures that the study is repeatable for others. TRUE FALSE

Which of the following questionnaire questions likely to be leading questions?

• Do you, or do you not, believe that permeable pavements are a viable alternative to traditional pavements? TRUE FALSE
• Do you support a ban on bottled water? TRUE FALSE
• Do you believe that double-gloving by paramedics reduces the risk of infection, increases the risk of infection, or makes no difference to the risk of infection? TRUE FALSE
• Should Ireland ban breakfast cereals with unhealthy sugar levels? TRUE FALSE

10.5 Exercises

Answers to odd-numbered exercises are available in App.  E .

Exercise 10.1 What is the problem with this question?

What is your age? (Select one option) Under \(18\) Over \(18\)

Exercise 10.2 What is the problem with this question?

How many children do you have? (Select one option) None 1 or 2 2 or 3 More than 4

Exercise 10.3 Which of these questionnaire questions is better? Why?

• Should concerned cat owners vaccinate their pets?
• Should domestic cats be required to be vaccinated or not?
• Do you agree that pet-owners should have their cats vaccinated?

Exercise 10.4 Which of these questionnaire questions is better? Why?

• Do you own an environmentally-friendly electric vehicle?
• Do you own an electric vehicle?
• Do you own or do you not own an electric vehicle?

Exercise 10.5 Falk and Anderson ( 2013 ) studied sunscreen use, and asked participants questions, including these:

• How often do you sun bathe with the intention to tan during the summer in Sweden? (Possible answers: never, seldom, sometimes, often, always).
• How long do you usually stay in the sun between \(11\) am and \(3\) pm, during a typical day-off in the summer (June--August)? (Possible answers: \(<30\)  min, \(30\) min-- \(1\) h, \(1\) -- \(2\) h, \(2\) -- \(3\) h, \(>3\) h).

Critique these questions. What biases may be present?

Exercise 10.6 Morón-Monge, Hamed, and Morón Monge ( 2021 ) studied primary-school children's knowledge of their natural environment. They were asked three questions:

• No, I don’t like parks.
• No, I don’t usually visit it.
• Yes, once per week.
• Yes, more than once a week
• Two to three times
• More than three times
• Write a story
• Draw a picture

Which questions are open and which are closed ? Critique the questions.

How to Write a Data Collection Plan (Templates and Examples Included)

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

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

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

What is a data collection plan?

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

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

What does a typical data collection plan document cover

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

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

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

Ways to collect data

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

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

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

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

Steps for writing an effective data collection plan

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

1. Define objectives and research questions

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

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

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

2. Identify data requirements and availability

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

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

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

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

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

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

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

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

3. Choose how you will collect data

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

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

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

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

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

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

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

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

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

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

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

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

5. Decide how will data be analyzed and presented

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

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

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

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

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

Data collection plan examples and templates

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

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

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.

Structure of Research Methodology

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

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

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.

Qualitative Research Methodology

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.

Mixed-Methods Research Methodology

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.

Case Study Research Methodology

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.

Action Research Methodology

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.

Experimental Research Methodology

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.

Survey Research Methodology

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.

Grounded Theory Research Methodology

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.

Research Methodology Example

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.

How to Write Research Methodology

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:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write 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.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

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:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Procedures

• Open Access
• First Online: 28 March 2023

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• Ivan Buljan   ORCID: orcid.org/0000-0002-8719-7277 3  

Part of the book series: Collaborative Bioethics ((CB,volume 1))

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This chapter offers a guide on how to implement good research practices in research procedures, following the logical steps in research planning from idea development to the planning of analysis of collected data and data sharing. This chapter argues that sound research methodology is a foundation for responsible science. At the beginning of each part of the chapter, the subtitles are formulated as questions that may arise during your research process, in the attempt to bring the content closer to the everyday questions you may encounter in research. We hope to stimulate insight into how much we can predict about a research study before it even begins. Research integrity and research ethics are not presented as separate aspects of research planning, but as integral parts that are important from the beginning, and which often set the directions of research activities in the study.

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Ethical Issues in Research Methods

Research Integrity: Responsible Conduct of Research

• Research plan
• Research question
• Study design
• Measurement
• Protocol registration
• Reproducibility

What This Chapter Is About

Case scenario: planning research.

This hypothetical scenario was adapted from a narrative about the process of poor research planning and its consequences. The original case scenario is developed by the Members of The Embassy of Good Science and is available at the Embassy of Good Science . The case is published under the Creative Commons Attribution-ShareAlike License, version 4.0 (CC BY-SA 4.0).

Professor Gallagher is a leader of a research project on moral intuitions in the field of psychology. She is working on the project with Dr. Jones, a philosopher, and Mr. Singh, a doctoral student. Although she has little experience in the matter, Dr. Jones is put as the principal investigator in the study design and analysis of the two experiments, while Mr. Singh prepares materials and conducts the experiments.

After the first experimental study, Mr. Singh sends the results to Dr. Jones for analysis. After some time, eager to enter the results in his thesis, Singh asks Dr. Jones about the results of the study. She admits that she forgot to formulate the hypothesis before data analysis, and now the results can be interpreted as confirmatory, regardless of the direction. They decide to formulate a hypothesis that will result in a positive finding.

Mr. Singh and Dr. Jones present the results to Dr. Gallagher, who is satisfied and proceeds with paper writing. In the second study, Dr. Jones formulates multiple hypotheses before the study begins. Mr. Singh conducts the study and sends the results to Dr. Jones. She performs the analysis by trying to find only significant differences between groups. Finally, to achieve significance, she excludes participants over 60 years from the analysis and while presenting the results, admits that to Prof Gallagher. Prof Galagher is happy about the results and proceeds with the paper writing, while Mr. Singh enters the results in his dissertation.

Before Mr. Singh has the public defense of his dissertation, one of the internal reviewers notices that some data has been excluded from the second study and only significant results were reported. She invites Mr. Singh for an examination board meeting during which MR Singh admits that the data has been excluded and that in the first study hypothesis was formulated after the results were known.

Questions for You

Why is hypothesizing after the results are known, as described in the first study, considered problematic?

What was wrong about reporting only significant results in Study 2?

How would you improve the entire research process described in the scenario?

Good research practice from the European Code of Conduct for Research Integrity:

Researchers take into account the state-of-the-art in developing research ideas.

Researchers make proper and conscientious use of research funds.

What to Do First When You Have an Idea?

It is difficult to come up with a good research idea, and if you struggle to come up with a new research direction, that is perfectly fine. Creative processes are the highest form of learning and developing an idea requires significant cognitive effort. In some cases, you may have an epiphany, where you would suddenly come up with a great idea for your research project. This is something popularized by stereotypes about scientists as eccentric figures who come up with brilliant ways of tackling things using only their intelligence and intuition. However, scientific work resembles ore mining. It takes a tremendous effort to read relevant scientific literature, communicate with your peers, plan, and, in some cases, attempt and fail before you even start digging for gold. As in a mine, you will need to dig a lot of rocks before you come across diamonds and gold.

Usually, the most important decisions are made before digging even begins. To decide where you will start mining, you start with the exploration of the terrain. In research, this means knowing your field of study. You may read an interesting piece in the scientific literature or listen to a presentation at a conference and then think of a hypothesis whose testing will answer an interesting and important question in your research field. On the other hand, sometimes you have to adjust your research interest so that they fit the specific aims of grant funding calls. It does not matter what the source of the idea is, there are always two things to consider when developing research ideas: the current state of the field and the resources available to you. Good research practice is to consider the state of the art in developing your research ideas and make the proper use of research funds. This does not mean that you are not allowed to develop research ideas if they address a research topic that has been neglected. It is the responsibility of a researcher to combine the best of the “old” evidence with new research developments. It is important to keep in mind that research is not performed in a vacuum and that the funds and resources provided by public or private funders are given with an expectation of an honest answer to a specific research question. The main responsibility for the proper use of research funds is on the researcher, and this is overseen by funders during and at the end of the proposal. Another recommendation refers to the use of state-of-the-art information as a basis for your research. The control system in this case is other scientists who read or evaluate your research, and who will recognize outdated research results.

Let’s get back to the analogy of the mine for a moment. If you are paid to dig in the mine, you are expected to find important ore. In our case, a research funder is an employer, and the researchers are workers who need to go down the mine and get their hands dirty in the search for new true information. If you are set to dig a deep hole in the ground with the possibility of finding gold and diamonds, but you do not get any guarantee that you will find them unless you chose an appropriate place in a specific period, you would probably spend a lot of time planning and trying to decide where to start digging, what to do when specific problems arise and to avoid ending with a huge number of worthless rocks instead of gold and diamonds. The process is similar to research planning since a significant amount of the research process can be defined before data collection begins. As valuable as it can be, a research idea is just a thought which needs to be translated into research practice to gain its full impact.

How to Formulate a Good Research Question?

Research is performed to answer a specific question. The research process can be observed as a complex tool that, if used properly, can give a clear answer to a posed question. The research question is the compass of the research process (or the mine if we continue with our mine analogy) since it determines the steps of the research process. It translates into specific research aims and, consequently, into testable research hypotheses. Formulation of a research question is a skill that develops over time, a skill that can be learned. Your research question should have a FINER structure, which stands for: F easible, I nteresting, N ovel, E thical and R elevant. Although initially developed as a set of recommendations for quantitative research, FINER recommendations can be applied to formulating a research question in any given field of science.

The feasibility of a research aim is often defined by time restrictions and funding because research is often burdened by deadlines and output requirements set by the funders. F easibility is also affected by the availability of technology, geographical restrictions, availability of participants, or availability of collaborators. If one considers all those factors, it is obvious that research interests play only a small part in the formulation of a research question. Ask yourself: What research can be published in an excellent journal if you have limited funds and only 1 year for research, with limited access to a specific technology? (Today, highly specialized experts may be a greater problem than the technology in question). You might experience that the formulation of the research question is mostly defined by non-research factors, because, in the end, it is better to have a completed than never-finished research.

There are other elements of the research question that are as important as feasibility. The first one to consider is E thics, which affects all parts of the research process due to its broad nature. If research is not ethical, then it should not be conducted. In a mining analogy, ethics is training and safety, which helps you to protect others and yourself during the entire process. To get back to the best research practices, researchers should make proper use of research funds and fulfill the basic research aim – the benefit to society. This also implies treating members of that society with respect, respecting their privacy and dignity, and being honest and transparent about the research process and results. Therefore, when determining the feasibility of a research study, ethics aspects are the first to consider, along with the objective factors of time, cost, and manpower.

I nterest, N ovelty, and R elevance from the FINER guidance are the elements of the research question that increase the chances of getting funding or the chances for a journal publication, and they are closely aligned. Regardless of the audience (researchers, publishers, non-experts), research should be new to be interesting and relevant. However, doing research just for the novelty’s sake is analogous to the digger who starts digging a new mine every couple of days. It gives you the thrill of a new beginning, but you have not dug deep enough to get to the real results. Relevance, defined in this context as a significant add-on to the current knowledge, can be assessed with a high probability of success by a thorough search for available evidence. The main aim of that process is to identify research or practice gaps that can be filled to improve general knowledge.

Interest is related to the principal internal motivation of an individual to pursue research goals. The interest to pursue research aims is difficult to assess. When planning research, do you consider that research is interesting to you, your peers, potential users, or all three? Probably the last, but here is the catch. Interest is the most subjective part of research planning. Research planning could be understood as a balance between your interest and all other factors that affect the research outcome. A good research idea is often the compromise between objective possibilities and a desire to make a research discovery. If the research idea is interesting but extremely difficult (or even impossible) to conduct in given circumstances, you will end up frustrated. On the other hand, if you decide to perform research based solely on convenience (because it is something for which is easy to get funded or someone is offering you a research topic you are not interested in), it will be very difficult to stay motivated to complete the study.

The more structured your research question is, the easier it is to determine which research design is best to test the hypothesis and statistical analysis is more straightforward. Let’s look at several examples of research questions in biomedical research: Are psychedelics more effective in the treatment of psychosis than the standard treatment? What are the opinions of young fathers on exclusive breastfeeding of their spouses? Which percentage of the population has suffered from post-COVID-19 syndrome? Intuitively, for each of posed research questions, we would try to find answers differently. In cases of comparison of treatment methods and assessment of population percentage, we could express the results quantitatively, e.g., we could state explicitly how much the psychedelics treatment is better compared to standard methods in terms of days of remission or everyday functionality or an explicit number of people in the sample who had COVID-19-related symptoms. On the other hand, the answers to the question about the opinions of young fathers about exclusive breastfeeding are not straightforward or numerical, but more textual and descriptive. It is an example of the research question that would be more suitable for qualitative research. Qualitative and quantitative study designs answer different types of research questions and are therefore suitable for different situations. It is important to carefully consider and choose the most appropriate study design for your research question because only then can you get valid answers.

To conclude, research question development is the crucial factor in setting research direction. Although framed as a single sentence, it defines numerous parts of the research process, from research design to data analysis. On the other hand, non-research factors also have an equal role in research questions and need to be considered.

Literature Search

In a literature search, researchers go through the relevant information sources to systematically collect information, i.e. foreground knowledge, about a specific research phenomenon and/or procedure. While research information is readily available online not only to researchers but to the whole public, the skill of systematic literature search and critical appraisal of evidence is a specific research skill. A literature search is closely tied with the development of the research aim, because you may want to change it after you read about previous research.

When doing a literature search, you must be careful not to omit previous studies about the topic. Here we have two directions that must be balanced. The first one is to do a very precise search to find specific answers, and the other one is to perform a wide, sensitive search that will include many synonyms and combinations of words to discover articles that related to a specific term. Both of those approaches have their advantages and disadvantages: a precise search is less time-consuming and retrieves a small number of studies. However, it may omit important results, so you may end up performing studies for which we already have established conclusions. This creates waste in research because you will spend time and resources, and involve participants in unnecessary work, which would be unethical. You may also miss citing important studies. On the other hand, if you perform a search that is too wide, you will spend a lot of time filtering for useful articles, which leaves less time for doing research.

Researchers design, carry out, analyze and document research in a careful and well-considered manner.

Researchers report their results in a way that is compatible with the standards of the discipline and, where applicable, can be verified and reproduced.

What Is the Optimal Study Design for My Research?

Study designs are one of the main heuristics related to the reader’s perception of the credibility of research information. Also, different study designs give answers to different research questions. It is intuitively easy to understand that different approaches should be taken if the question is about the percentage of infected people in the population vs about which drug is the most effective in the treatment of the disease. The roughest categorization of the study designs is observational and experimental (Box 3.1 ). However, in different scientific areas, even that type of categorization is not enough, since study designs can be theoretical, as in physics or mathematics, or critical, as in humanities, and those types of research will not be covered in this chapter.

Box 3.1 Types of Study Designs

Observational study designs :.

Case study / case series / qualitative study : All three types of study designs take into account a small number of participants and examine the phenomenon of interest in-depth but cannot make generalizations about the entire population.

Case-control study : Individuals with a certain outcome or disease are selected and then information is obtained on whether the subjects have been exposed to the factor under investigation more frequently than the carefully selected controls. This approach is quick and cost-effective in the determination of factors related to specific states (e.g., risk factors), but it relies too much on records and/or self-report, which may be biased.

Cross-sectional study : Best study design for determining the prevalence and examination of relationships between variables that exist in the population at a specific time. Although it is simple to perform, and relatively cheap, it is susceptible to various types of bias related to participant selection, recall bias, and potential differences in group sizes.

Cohort study : Participants are followed over a certain period (retrospectively or prospectively) and data are compared between exposed and unexposed groups to determine predictive factors for the phenomenon of interest.

Experimental study designs :

Randomized controlled trial (RCT) : Participants are allocated to treatment or control groups using randomization procedures to test the strength of the interventions.

Quasi-experimental trial : Participants are allocated to treatment or control groups to test the strengths of the interventions, but there is no randomization procedure.

For some research areas (e.g. health sciences, social sciences), there is another type of research often referred to as evidence synthesis, or literature review. The literature review is a review of evidence-based on a formulated research question and elements. They differ in their scope and methodology (Box 3.2 ).

Box 3.2 Most Common Types of Review

Systematic review : A type of review that searches systematically for, appraises, and synthesizes research evidence, often adhering to guidelines on the conduct of a review.

Scoping review : Type of review which serves as a preliminary assessment of the potential size and scope of available research literature to identify the nature and extent of research evidence (usually including ongoing research).

Meta-analysis : Statistical synthesis of the results from quantitative studies to provide a more precise effect of the results.

Rapid review : A type of review that assesses what is already known about a policy or practice issue, by using systematic review methods to quickly search and critically appraise existing research to inform practical steps.

Umbrella review : Specific type of review that searches and assesses compiling evidence from multiple reviews into one accessible and usable document. Focuses on broad conditions or problems for which there are competing interventions and highlights reviews that address these interventions and their results.

How to Assess which Study Design Is Most Suitable for Your Research Question?

Based on the research aim, one may already get a hint about which study design will be applied, since different study designs give answers to different research questions. However, very often a research question is not so straightforward. Sometimes the research aim could be to determine whether category X is superior to category Y, related to the specific outcome. In those cases, one must determine what the core outcome of the study is (e.g., testing of the effectiveness of two interventions, the scores on current differences between two groups, or the changes over time between different groups), and then it is not difficult to determine the study type in question. In principle, a single research question can be answered with a single study design. However, what we can also use are substitute study designs that can give approximate answers to the question we are asking but will never give as clear an answer as the appropriate design. For example, if we want to explore the reasons early-career researchers seek training in research integrity using a survey approach, we could list all possible answers and say to participants to choose everything that applies to them. The more appropriate study design would be to use a qualitative approach instead because in the survey approach the assumption is that we already know most of the reasons. The survey approach gives us the answer which answer is the most frequent of all. It is a subtle, but important difference. Similarly, although we can test causation using a cohort approach, the evidence for causation is never strong enough in a cohort study as it would be in an experimental study, simply because in a cohort study the researcher does not have control over the independent variable. For example, if we would test the effects of alcohol uptake on the occurrence of cancer, we would compare participants who drink versus those who do not drink to determine the incidence of cancer and make the conclusion about the association between alcohol and cancer. However, the true study design for testing the causation is the randomized controlled trial, where participants are randomized into the interventional and control group, the researcher can give an exact amount of alcohol based on persons’ weight, over a specific period, and in the end, compare the incidence between two groups. However, that type of study would not be an ethical study, so it is not possible to do it. So, there are subtle, but important differences which answer whether can specific and good formulated research questions can be tested and answered fully with only one study design, but due to the various reasons (time restrictions, ethics, cost-benefit analysis) we often use substitute study designs.

When describing people involved in the research process, researchers often refer to them as “participants” or “respondents” (in the case of surveys). A more precise term would be to name the group based on the population they are drawn from (children, people with specific diseases, or people from a specific geographical area). The appropriate term to use would be “participants”, since people are willingly involved in the research process, and the generation of new findings depends on them. Being a participant in a research process means that a person has willingly entered into a research, without any real or imagined coercion, possesses respect and interest for the research topic, with the understanding that positive aspects of research findings encompass the research situation and contribute to general knowledge. This would be a definition of an ideal participant and the researcher should avoid a situation where the participants are coerced to enter research, whether by situational factors or personal reasons because that will probably result in a decrease in motivation for participation and lower quality of research findings. To act ethically and to improve the quality of the research you have to inform participants about the reasons for the study, its purpose, research procedure, their rights, and expected outcomes. A potential pitfall in the research process can happen if all information were not given to participants at the beginning of a research. On the other hand, if a participant enters willingly into the trial, but possesses no real interest in the research topic, it may also affect the motivation for participation in research, because those participants may consider the topic irrelevant and not take the research process seriously (it is easy to imagine a situation where teenagers in a classroom willingly decide to take the survey and participate in research about personality traits, but quickly lose interest after the second page of the questionnaire). All those things are not reflected in the research report but may have an enormous influence on the research findings. Therefore, it is important to define the population of interest and try to motivate participants by providing them with all information before the research begins. Some additional ways to increase participant retention are financial rewards or similar incentives. There are several sampling strategies used when approaching participants for a study (Box 3.3 ).

Box 3.3 Most Common Sampling Methods

Simple random sampling : Each member of the defined population has an equal chance of being included in the study. The sampling is often performed by a coin toss, throwing dice, or (most commonly) using a computer program.

Stratified random sampling : The population of interest is first divided into strata (subgroups) and then we perform random sampling from each subgroup. In this way, the sample with better reflects the target population in specific (relevant) characteristics.

Cluster random sampling : In cluster sampling, the parts of the population (subgroups) are used as sampling units instead of individuals.

Systematic sampling : Participants are selected by equal intervals set before the data collection begins (e.g., every third of every fifth participant who enters the hospital).

Convenience sampling : Participants are approached based on availability. This is perhaps the most common sampling method, especially for survey research.

Purposive sampling : This is the most common approach in qualitative study designs. Researchers choose participants (or they define their characteristics in detail), based on their needs since participants with those special characteristics are the research topic.

It is difficult to give clear criteria on when to stop collecting data. In the case of pre-registered studies, the stopping rule is defined in the protocol. Examples include time restrictions (e.g. 1 month), or the number of participants (e.g. after collecting data on 100 participants). If the research protocol has not been pre-registered, then the stopping rule should be explained in detail in the publication, with reasons. In the latter case, it is never completely clear if the stopping happened after researchers encountered the desired result or if it has been planned. The practice of stopping after you collect sufficient data to support your desired hypothesis is highly unethical since it can lead to biased findings. Therefore, the best way of deciding to terminate the data collection is to pre-register your study, or at least define the desired number of participants by performing sample size calculation before the study begins and pre-registering your study. More about pre-registration and biases which it eliminates will be said later in the chapter.

Ethics of the Sample Size: Too Small and Too Big Samples

A common problem in sampling is that researchers often determine the desired number of participants in a study. The problem is that the response rate is always lower than 100% (in survey research it is often around 15–20%), and a certain percentage of participants drops out of research, resulting in a sample size significantly lower than initially planned. The sample used in research can be too small, and there is a possibility that you will not find a true effect between groups, and in that case, you would make a type II error. The reason is that in small-scale studies the error margin is big, and you would need an extremely large effect size to reach statistical significance. On the other hand, in cases of a big sample, the problem is different. If you have big samples, even small effects will be statistically significant, but the effect size may be negligible. The reason is that within big samples, the margin of error is small, and consequently, every difference is statistically significant. Once again, the proper solution (practically and ethically) for this issue is to calculate the minimum sample size needed to determine the desired difference between groups to avoid the issues with small samples and report effect sizes also, to avoid issues related to (too) big samples.

What We Can and What Cannot Measure?

When it comes to measuring in research, that part is mostly associated with statistical analysis of research data. The principal thing in statistical analysis is to determine the nature of the main outcome variable. In qualitative research (e.g. interview, focus group) or a systematic review without meta-analysis, statistical analysis is not necessary. On the other hand, for quantitative studies (a term often used for mostly case-control, cross-sectional, cohort, and interventional studies) the most important part of the research plan is to define the outcome which can be measured.

In general, there are two types of variables: qualitative and quantitative. When it comes to statistical analysis of qualitative variables (in a statistical context you will encounter the terms nominal and ordinal variables), we can do only basic functions, like counting and comparing the proportions between different groups, but we are not able to calculate mean or standard deviation, because those variables do not possess numerical characteristics. Examples of qualitative variables in research can be the number of surviving patients in a group at the end of the trial, self-reported socioeconomic status as a demographic characteristic, or any binary (yes/no) question in a questionnaire. In some cases, qualitative variables may be coded with numbers, but that does not make them quantitative. A good example is jersey numbers where numbers serve only as a label and not as a measure of quantity (e.g. if you have team player numbers 2, 4, 6, you probably will not state that the average jersey number is 4 because the very concept of the “average” jersey number is absurd). On the other hand, for quantitative variables, differences between numbers indicate the differences in value (e.g. if you say that person X is 1.80 m high, you know that that person is taller than person Y who is 1.70 m tall). You can also calculate different statistical parameters, like mean and median, and dispersion measures, which gives you a more flexible approach in the choice of statistical tests, especially those tests for differences between groups. On the other hand, applying statistical tests would mean that you are more familiar with statistics, which sometimes may present a problem for less (and more) experienced researchers.

When Is the Time to Consult with a Statistician (and Do You Have to)?

Some (lucky) researchers possess sufficient knowledge to perform data analysis themselves. They usually do not need to rely on somebody else to do the statistical analysis for their study. For everybody else, statistical analysis is a crossroad where one needs to decide on including a person with statistical knowledge in a research team or to learn statistical analyses by themselves. The usual process is that the research team defines the research aim, spends time collecting data, collects data, and then tries to find a statistician who will analyse the data. If we keep in mind that research often has high stakes (e.g. doctoral diploma) and researchers are under a great time and financial pressure, the decision to include a statistician is sound and logical, but is it really necessary? The inclusion of a statistician in research when the data are already collected is similar to the situation when you give a cook an already finished stew and ask him/her how it can be improved. The cook may help with the decorations and give some spice which would make the food look and taste better but cannot change the essence of the food since it is already cooked. It is the same with data. The golden rule of statistics is “garbage in, garbage out”, referring to a situation where poorly collected data or data of poor quality will give rise to wrong conclusions. Researchers should know statistics, not only because of the statistical analysis but because statistical reasoning is important in the formulation of measurable research aims. Therefore, statistical analysis is an important part of responsible research and begins with the formulation of the research aim. Statistical experts should be included in the study at that point.

Statisticians usually analyse data based on the initially set research aim. They send back the results of the data analysis to the research team, and they all together (in an ideal scenario) write the manuscript. The dataset remains in the possession of the principal researcher and the paper is published in a journal. Many journals and funders require that the data are publicly available so that anyone can use it, respecting the FAIR principles. Keeping that in mind, the process when somebody else is doing statistical analysis for you requires an enormous level of trust for statisticians, because they can do analysis wrong but you may never know it. Unless, of course, someone else analyses publicly available data and sees the error. In that case, you are also responsible for the analysis because it does not matter that you did not perform it. In some cases, this may lead to the retraction of the paper, which consequently may lead to certain consequences for you (especially if the articles are the basis for a doctoral thesis). If you are willing to put trust in someone to do data analysis, that is perfectly fine, just be aware of this risk, and remember that people make mistakes, very often unintentionally, and therefore a double check by a third party would be recommended.

On the other hand, if you are willing to learn how to do statistical analysis, the good news is that today there are lots of resources to help you. The first thing about statistics you need to know is that you do not need to know all statistics to do statistics. The only knowledge about statistics and statistical programs you need is the one that would help you do the analysis of your research aim and test the research hypothesis. To do that, you will have to see the data you have and search online for ways to analyze a specific problem. You can use tutorials of the statistical program that simultaneously give instructions about the statistical principles and procedures for analysis. Today, most of those programs have online videos and detailed tutorials. Some of those programs are user-friendly and free (e.g., JAMOVI or JASP ), some are commercial (e.g., SPSS, Statistica), and some are less user-friendly but free and available (e.g., R programming language ). If you are a beginner, use a more user-friendly program that has detailed instructions and try to do the statistical analysis by yourself. It is expected that you will make errors, so it would be good if someone more experienced looked at the results and provides feedback on your first attempts.

There are many tutorials on how to do statistical analysis, but far less on how to do proper data entry, which is the preparation of data for statistical analysis. Usually, the data entry table is made in a computer program that provides a tabular view of the data (e.g., Microsoft Excel). The golden rule is that each column represents a variable collected in research, by the order it was collected in the research and that each row represents the unit of the analysis (usually participant, text, article, or any other unit). In a separate sheet or a document, there should be a codebook that contains information about each level of each variable in the dataset, in a way that a person who is not familiar with research can understand the nature of the variable. The codebook should always accompany the dataset, so if the dataset is shared publicly, the codebook should also be shared. The rule of thumb for the data entry is that textual variables are entered as texts and quantitative variables as numbers, and textual variables can later be coded with numbers if necessary. The table for data entry should be made before the research begins, and it is good to seek help from a statistician when defining that, too.

Researchers publish results and interpretations of research in an open, honest, transparent, and accurate manner, and respect the confidentiality of data or findings when legitimately required to do so.

Preregistration of Research Findings

Pre-registration refers to the presentation of the research plan before the research begins. This process serves as the quality control mechanism because it prevents a change in the research hypothesis and methodology to fit the data collected. Pre-registration of research findings should be done after the research has been approved by the ethics committee. There are various registries, some of which are more discipline-specific (e.g., ClinicalTrials.gov for clinical studies) while others are open to different disciplines and study designs (e.g., Open Science Framework ). For the pre-registration of a study, one should clearly define all steps related to the research aim, methods, planned analysis, and planned use of data. Pre-registration of data is nothing more than the public sharing of a research plan. However, even that relatively simple procedure helps eliminate specific biases and decreases the probability of unethical behavior. Pre-registration eliminates the problem of h ypothesizing a fter the r esults are k nown (so-called HARKing) because you need to state your hypothesis publicly before the research begins. Pre-registration should be done before the actual research begins, since you may have already collected the data and modified your hypothesis so that it fits your data (this is called PARKing – p re-registering a fter the r esults are k nown), which should be avoided since it is not a true pre-registration.

Why is pre-registration good for research? When a study is pre-registered, researchers will follow the research plan and planned analysis and will not alter the study protocol and statistical analysis unless there is a valid and strong reason for protocol modification. Many journals today require that studies are pre-registered and that research data are shared. It is recommended to pre-register not only the study aim and methods, planned analysis, but also planned impact, data use, and authorship. When pre-registering authorship, you make clear from the beginning of the study the roles and expectations of each member of the research team. If during the research process some changes happen with the study protocol, those should be clearly explained and pointed out in the final publication, because deviations from the protocol can sometimes bring suspicion in the interpretation of the results if they are not reported. Pre-registration can be peer-reviewed and some problems, which would affect the final interpretation of the results, can be addressed even before the study begins. Finally, when pre-registered, you have the evidence that it was you who came up first with a specific research idea.

One problem that pre-registration cannot prevent is research spin or exaggeration in the scope of study results. Even if data have been carefully collected and properly analyzed, the interpretation of the results is up to the researcher. You should be honest (and modest) when interpreting the results of your study, by stating the true magnitude of your results and putting them in the context of the previous studies.

After the research has been published, the data used in research should be made available to everyone who wants to use them, since data sharing helps research replication and evidence synthesis. You can read more about data sharing in the chapter on Data Management and the chapter on Publication and Dissemination.

With this knowledge in mind, how would you improve the research procedure from the case scenario at the beginning of this chapter?

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Ana Marusic

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Replicability

AllTrials campaign: https://embassy.science/wiki/Theme:0bb5e4f7-9336-4ca8-92e3-c506413d1450

Forensic statistics to detect data fabrication: https://embassy.science/wiki/Theme:467f5cf6-d41f-42a0-9b19-76556579845d

Pre-registration of animal study protocols

Prospective registration of clinical trials

Statistical pre-registration

Data driven hypothesis without disclosure (“HARKing”)

Insufficiently reported study flaws and limitations

Spin of research results .

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Buljan, I. (2023). Research Procedures. In: Marusic, A. (eds) A Guide to Responsible Research. Collaborative Bioethics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-22412-6_3

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