what is data collection in research

Table of Contents

What is data collection, why do we need data collection, what are the different data collection methods, data collection tools, the importance of ensuring accurate and appropriate data collection, issues related to maintaining the integrity of data collection, what happens after data collection, what are common challenges in data collection, what are the key steps in the data collection process, data collection considerations and best practices, choose the right data science program, what is data collection: methods, types, tools.

The process of gathering and analyzing accurate data from various sources to find answers to research problems, trends and probabilities, etc., to evaluate possible outcomes is known as data collection. Knowledge is power, information is knowledge, and data is information in digitized form, at least as defined in IT. Hence, data is power. But before you can leverage that data into a successful strategy for your organization or business, you need to gather it. That’s your first step.

So, to help you get the process started, we shine a spotlight on data collection. What exactly is it? Believe it or not, it’s more than just doing a Google search! Furthermore, what are the different types of data collection? And what kinds of data collection tools and data collection techniques exist? If you want to get up to speed about what is data collection process, you’ve come to the right place. Let's start!

Data collection is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. It is an essential phase in all types of research, analysis, and decision-making, including that done in the social sciences, business, and healthcare.

During data collection,  researchers must identify the data types, the sources of data, and the methods being used. We will soon see that there are many different  data collection methods . Data collection is heavily reliance on  in research, commercial, and government fields.

Before an analyst begins collecting data, they must answer three questions first:

• What’s the goal or purpose of this research?
• What kinds of data are they planning on gathering?
• What methods and procedures will be used to collect, store, and process the information?

Additionally, we can divide data into qualitative and quantitative types. Qualitative data covers descriptions such as color, size, quality, and appearance. Unsurprisingly, quantitative data deals with numbers, such as statistics, poll numbers, percentages, etc.

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Before a judge makes a ruling in a court case or a general creates a plan of attack, they must have as many relevant facts as possible. The best courses of action come from informed decisions, and information and data are synonymous.

The concept of data collection isn’t new, as we’ll see later, but the world has changed. There is far more data available today, and it exists in forms that were unheard of a century ago. The data collection process has had to change and grow, keeping pace with technology.

Whether you’re in academia, trying to conduct research, or part of the commercial sector, thinking of how to promote a new product, you need data collection to help you make better choices.

Now that you know what data collection is and why we need it, let's look at the different methods of data collection. Data collection could mean a telephone survey, a mail-in comment card, or even some guy with a clipboard asking passersby some questions. But let’s see if we can sort the different data collection methods into a semblance of organized categories.

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Primary and secondary methods of data collection are two approaches used to gather information for research or analysis purposes. Let's explore each data collection method in detail:

1. Primary Data Collection

The first techniques of data collection is Primary data collection which involves the collection of original data directly from the source or through direct interaction with the respondents. This method allows researchers to obtain firsthand information tailored to their research objectives. There are various techniques for primary data collection, including:

a. Surveys and Questionnaires: Researchers design structured questionnaires or surveys to collect data from individuals or groups. These can be conducted through face-to-face interviews, telephone calls, mail, or online platforms.

b. Interviews: Interviews involve direct interaction between the researcher and the respondent. They can be conducted in person, over the phone, or through video conferencing. Interviews can be structured (with predefined questions), semi-structured (allowing flexibility), or unstructured (more conversational).

c. Observations: Researchers observe and record behaviors, actions, or events in their natural setting. This method is useful for gathering data on human behavior, interactions, or phenomena without direct intervention.

d. Experiments: Experimental studies involve manipulating variables to observe their impact on the outcome. Researchers control the conditions and collect data to conclude cause-and-effect relationships.

e. Focus Groups: Focus groups bring together a small group of individuals who discuss specific topics in a moderated setting. This method helps in understanding the opinions, perceptions, and experiences shared by the participants.

2. Secondary Data Collection

The next techniques of data collection is Secondary data collection which involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information. Secondary data can be obtained from various sources, including:

a. Published Sources: Researchers refer to books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data.

b. Online Databases: Numerous online databases provide access to a wide range of secondary data, such as research articles, statistical information, economic data, and social surveys.

c. Government and Institutional Records: Government agencies, research institutions, and organizations often maintain databases or records that can be used for research purposes.

d. Publicly Available Data: Data shared by individuals, organizations, or communities on public platforms, websites, or social media can be accessed and utilized for research.

e. Past Research Studies: Previous research studies and their findings can serve as valuable secondary data sources. Researchers can review and analyze the data to gain insights or build upon existing knowledge.

Now that we’ve explained the various techniques let’s narrow our focus even further by looking at some specific tools. For example, we mentioned interviews as a technique, but we can further break that down into different interview types (or “tools”).

Word Association

The researcher gives the respondent a set of words and asks them what comes to mind when they hear each word.

Sentence Completion

Researchers use sentence completion to understand the respondent's ideas. This tool involves giving an incomplete sentence and seeing how the interviewee finishes it.

Role-Playing

Respondents are presented with an imaginary situation and asked how they would act or react if it were real.

In-Person Surveys

The researcher asks questions in person.

Online/Web Surveys

These surveys are easy to accomplish, but some users may be unwilling to answer truthfully, if at all.

Mobile Surveys

These surveys take advantage of the increasing proliferation of mobile technology. Mobile collection surveys rely on mobile devices like tablets or smartphones to conduct surveys via SMS or mobile apps.

Phone Surveys

No researcher can call thousands of people at once, so they need a third party to handle the chore. However, many people have call screening and won’t answer.

Observation

Sometimes, the simplest method is the best. Researchers who make direct observations collect data quickly and easily, with little intrusion or third-party bias. Naturally, this method is only effective in small-scale situations.

Accurate data collecting is crucial to preserving the integrity of research, regardless of the subject of study or preferred method for defining data (quantitative, qualitative). Errors are less likely to occur when the right data gathering tools are used (whether they are brand-new ones, updated versions of them, or already available).

Among the effects of data collection done incorrectly include the following:

• Erroneous conclusions that squander resources
• Decisions that compromise public policy
• Incapacity to correctly respond to research inquiries
• Bringing harm to participants who are humans or animals
• Deceiving other researchers into pursuing futile research avenues
• The study's inability to be replicated and validated

When these study findings are used to support recommendations for public policy, there is the potential to result in disproportionate harm, even if the degree of influence from flawed data collecting may vary by discipline and the type of investigation.

Let us now look at the various issues that we might face while maintaining the integrity of data collection.

To assist the error detection process in the data gathering process, whether they were done purposefully (deliberate falsifications) or not, maintaining data integrity is the main justification (systematic or random errors).

Quality assurance and quality control are two strategies that help protect data integrity and guarantee the scientific validity of study results. Each strategy is used at various stages of the research timeline:

• Quality control - tasks that are performed both after and during data collecting
• Quality assurance - events that happen before data gathering starts

Let us explore each of them in more detail now.

Quality Assurance

As data collecting comes before quality assurance, its primary goal is "prevention" (i.e., forestalling problems with data collection). The best way to protect the accuracy of data collection is through prevention. The uniformity of protocol created in the thorough and exhaustive procedures manual for data collecting serves as the best example of this proactive step. 

The likelihood of failing to spot issues and mistakes early in the research attempt increases when guides are written poorly. There are several ways to show these shortcomings:

• Failure to determine the precise subjects and methods for retraining or training staff employees in data collecting
• List of goods to be collected, in part
• There isn't a system in place to track modifications to processes that may occur as the investigation continues.
• Instead of detailed, step-by-step instructions on how to deliver tests, there is a vague description of the data gathering tools that will be employed.
• Uncertainty regarding the date, procedure, and identity of the person or people in charge of examining the data
• Incomprehensible guidelines for using, adjusting, and calibrating the data collection equipment.

Now, let us look at how to ensure Quality Control.

Quality Control

Despite the fact that quality control actions (detection/monitoring and intervention) take place both after and during data collection, the specifics should be meticulously detailed in the procedures manual. Establishing monitoring systems requires a specific communication structure, which is a prerequisite. Following the discovery of data collection problems, there should be no ambiguity regarding the information flow between the primary investigators and staff personnel. A poorly designed communication system promotes slack oversight and reduces opportunities for error detection.

Direct staff observation conference calls, during site visits, or frequent or routine assessments of data reports to spot discrepancies, excessive numbers, or invalid codes can all be used as forms of detection or monitoring. Site visits might not be appropriate for all disciplines. Still, without routine auditing of records, whether qualitative or quantitative, it will be challenging for investigators to confirm that data gathering is taking place in accordance with the manual's defined methods. Additionally, quality control determines the appropriate solutions, or "actions," to fix flawed data gathering procedures and reduce recurrences.

Problems with data collection, for instance, that call for immediate action include:

• Fraud or misbehavior
• Systematic mistakes, procedure violations 
• Individual data items with errors
• Issues with certain staff members or a site's performance 

Researchers are trained to include one or more secondary measures that can be used to verify the quality of information being obtained from the human subject in the social and behavioral sciences where primary data collection entails using human subjects. 

For instance, a researcher conducting a survey would be interested in learning more about the prevalence of risky behaviors among young adults as well as the social factors that influence these risky behaviors' propensity for and frequency. Let us now explore the common challenges with regard to data collection.

Once you’ve gathered your data through various methods of data collection, here is what happens next:

Process and Analyze Your Data

At this stage, you’ll use various methods to explore your data more thoroughly. This can involve statistical methods to uncover patterns or qualitative techniques to understand the broader context. The goal is to turn raw data into actionable insights that can guide decisions and strategies moving forward.

Interpret and Report Your Results

After analyzing the data collected through methods of data collection in research, the next step is to interpret and present your findings. The format and detail depend on your audience, researchers might require academic papers, M&E teams need comprehensive reports, and field teams often rely on real-time feedback. What’s key here is ensuring that the data is communicated clearly, allowing everyone to make informed decisions.

Safely Store and Handle Data

Once your data has been analyzed, proper storage is essential. Cloud storage is a reliable option, offering both security and accessibility. Regular backups are also important, as is limiting access to ensure that only the right people are handling sensitive information. This helps maintain the integrity and safety of your data throughout the project.

Some prevalent challenges are faced while collecting data. Let us explore a few of them to better understand them and avoid them.

Data Quality Issues

The main threat to the broad and successful application of machine learning is poor data quality. Data quality must be your top priority if you want to make technologies like machine learning work for you. Let's talk about some of the most prevalent data quality problems in this blog article and how to fix them.

Inconsistent Data

When working with various data sources, it's conceivable that the same information will have discrepancies between sources. The differences could be in formats, units, or occasionally spellings. The introduction of inconsistent data might also occur during firm mergers or relocations. Inconsistencies in data tend to accumulate and reduce the value of data if they are not continually resolved. Organizations that focus heavily on data consistency do so because they only want reliable data to support their analytics.

Data Downtime

Data is the driving force behind the decisions and operations of data-driven businesses. However, there may be brief periods when their data is unreliable or not prepared. Customer complaints and subpar analytical outcomes are only two ways this data unavailability can significantly impact businesses. A data engineer spends significant amount of their time updating, maintaining, and guaranteeing the integrity of the data pipeline. To ask the next business question, there is a high marginal cost due to the lengthy operational lead time from data capture to insight.

Schema modifications and migration problems are just two examples of the causes of data downtime. Due to their size and complexity, data pipelines can be difficult to manage. Data downtime must be continuously monitored and reduced through automation.

Ambiguous Data

Even with thorough oversight, some errors can still occur in massive databases or data lakes. The issue becomes more overwhelming when data streams at a fast speed. Spelling mistakes can go unnoticed, formatting difficulties can occur, and column heads might be deceptive. This unclear data might cause several problems for reporting and analytics.

Duplicate Data

Streaming data, local databases, and cloud data lakes are just a few of the data sources that modern enterprises must contend with. They might also have application and system silos. These sources are likely to duplicate and overlap each other quite a bit. For instance, duplicate contact information has a substantial impact on customer experience. Marketing campaigns suffer if certain prospects are ignored while others are engaged repeatedly. The likelihood of biased analytical outcomes increases when duplicate data are present. It can also result in ML models with biased training data.

Abundance of Data

While we emphasize data-driven analytics and its advantages, a data quality problem with excessive data exists. There is a risk of getting lost in abundant data when searching for information pertinent to your analytical efforts. Data scientists, data analysts, and business users devote 80% of their work to finding and organizing the appropriate data. With increased data volume, other problems with data quality become more serious, mainly when dealing with streaming data and significant files or databases.

Inaccurate Data

Data accuracy is crucial for highly regulated businesses like healthcare. Given the current experience, it is more important than ever to increase the data quality for COVID-19 and later pandemics. Inaccurate information does not provide a true picture of the situation and cannot be used to plan the best course of action. Personalized customer experiences and marketing strategies underperform if your customer data is inaccurate.

Data inaccuracies can be attributed to several things, including data degradation, human mistakes, and data drift. Worldwide data decay occurs at a rate of about 3% per month, which is quite concerning. Data integrity can be compromised while transferring between different systems, and data quality might deteriorate with time.

Hidden Data

The majority of businesses only utilize a portion of their data, with the remainder sometimes being lost in data silos or discarded in data graveyards. For instance, the customer service team might not receive client data from sales, missing an opportunity to build more precise and comprehensive customer profiles. Missing out on possibilities to develop novel products, enhance services, and streamline procedures is caused by hidden data.

Finding Relevant Data

Finding relevant data is not so easy. There are several factors that we need to consider while trying to find relevant data, which include -

• Relevant Domain
• Relevant demographics
• We need to consider Relevant Time periods and many more factors while trying to find appropriate data.

Data irrelevant to our study in any of the factors renders it obsolete, and we cannot effectively proceed with its analysis. This could lead to incomplete research or analysis, re-collecting data repeatedly, or shutting down the study.

Deciding the Data to Collect

Determining what data to collect is one of the most important factors while collecting data and should be one of the first factors in collecting data. We must choose the subjects the data will cover, the sources we will use to gather it, and the required information. Our responses to these queries will depend on our aims, or what we expect to achieve utilizing your data. As an illustration, we may choose to gather information on the categories of articles that website visitors between the ages of 20 and 50 most frequently access. We can also decide to compile data on the typical age of all the clients who purchased from your business over the previous month.

Not addressing this could lead to double work, the collection of irrelevant data, or the ruin of your study.

Dealing With Big Data

Big data refers to massive data sets with more intricate and diversified structures. These traits typically result in increased challenges while storing, analyzing, and using additional methods of extracting results. Big data refers especially to data sets that are so enormous or intricate that conventional data processing tools are insufficient. The overwhelming amount of data, both unstructured and structured, that a business faces daily. 

Recent technological advancements have increased the amount of data produced by healthcare applications, the Internet, social networking sites, sensor networks, and many other businesses.

Low Response and Other Research Issues

Poor design and low response rates were shown to be two issues with data collecting, particularly in health surveys that used questionnaires. This might lead to an insufficient or inadequate data supply for the study. Creating an incentivized data collection program might be beneficial in this case to get more responses.

Now, let us look at the critical steps in the data collection process.

In the Data Collection Process, there are five key steps. They are explained briefly below:

1. Decide What Data You Want to Gather

The first thing that we need to do is decide what information we want to gather. We must choose the subjects the data will cover, the sources we will use to collect it, and the quantity of information that we will require. For instance, we may choose to gather information on the categories of products that an average e-commerce website visitor between the ages of 30 and 45 most frequently searches for. 

2. Establish a Deadline for Data Collection

The process of creating a strategy for data collection can now begin. We should set a deadline for our data collection at the outset of our planning phase. Some forms of data we might want to collect continuously. For instance, we might want to build up a technique for tracking transactional data and website visitor statistics over the long term. However, we will track the data throughout a certain time frame if we are tracking it for a particular campaign. In these situations, we will have a schedule for beginning and finishing gathering data. 

3. Select a Data Collection Approach

At this stage, we will select the data collection technique to serve as the foundation of our data-gathering plan. We must consider the type of information we wish to gather, the period we will receive it, and the other factors we decide on when choosing the best gathering strategy.

4. Gather Information

Once our plan is complete, we can implement our data collection plan and begin gathering data. In our DMP, we can store and arrange our data. We need to be careful to follow our plan and keep an eye on how it's doing. Especially if we are collecting data regularly, setting up a timetable for when we will be checking in on how our data gathering is going may be helpful. As circumstances alter and we learn new details, we might need to amend our plan.

5. Examine the Information and Apply Your Findings

It's time to examine our data and arrange our findings after gathering all our information. The analysis stage is essential because it transforms unprocessed data into insightful knowledge that can be applied to better our marketing plans, goods, and business judgments. The analytics tools included in our DMP can assist with this phase. We can put the discoveries to use to enhance our business once we have discovered the patterns and insights in our data.

Let us now look at some data collection considerations and best practices that one might follow.

We must carefully plan before spending time and money traveling to the field to gather data. While saving time and resources, effective data collection strategies can help us collect richer, more accurate, and richer data.

Below, we will be discussing some of the best practices that we can follow for the best results:

1. Take Into Account the Price of Each Extra Data Point

Once we have decided on the data we want to gather, we need to consider the expense of doing so. Our surveyors and respondents will incur additional costs for each additional data point or survey question.

2. Plan How to Gather Each Data Piece

There is a dearth of freely accessible data. Sometimes the data is there, but we may not have access to it. For instance, unless we have a compelling cause, we cannot openly view another person's medical information. It could be challenging to measure several types of information.

Consider how time-consuming and complex it will be to gather each piece of information while deciding what data to acquire.

3. Think About Your Choices for Data Collecting Using Mobile Devices

Mobile-based data collecting can be divided into three categories -

• IVRS (interactive voice response technology) -  Will call the respondents and ask them questions that have already been recorded. 
• SMS data collection - Will send a text message to the respondent, who can then respond to questions by text on their phone. 
• Field surveyors - Can directly enter data into an interactive questionnaire while speaking to each respondent, thanks to smartphone apps.

We need to select the appropriate tool for our survey and respondents because each has its own disadvantages and advantages.

4. Carefully Consider the Data You Need to Gather

It's all too easy to get information about anything and everything, but it's crucial only to gather the information we require. 

It is helpful to consider these three questions:

• What details will be helpful?
• What details are available?
• What specific details do you require?

5. Remember to Consider Identifiers

Identifiers, or details describing the context and source of a survey response, are just as crucial as the information about the subject or program that we are researching.

Adding more identifiers will enable us to pinpoint our program's successes and failures more accurately, but moderation is the key.

6. Data Collecting Through Mobile Devices is the Way to Go

Although collecting data on paper is still common, modern technology relies heavily on mobile devices. They enable us to gather various data types at relatively lower prices and are accurate and quick. With the boom of low-cost Android devices, there aren't many reasons not to choose mobile-based data collecting.

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To sum up, it is vital to master data collection for making decisions that are well-informed and conducting effective research. Once you understand the different data collection techniques and know about the right tools and best practices, you can gather meaningful and accurate data. However, you must address the common challenges and concentrate on the essential steps involved in the process to maintain your data's credibility and achieve good results.

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1. What is data collection with example?

Data collection is the process of collecting and analyzing information on relevant variables in a predetermined, organized way so that one can respond to specific research questions, test hypotheses, and assess results. Data collection can be either qualitative or quantitative. For example, a company collects customer feedback through online surveys and social media monitoring to improve its products and services.

2. What are the primary data collection methods?

As is well known, gathering primary data is costly and time intensive. The main techniques for collecting data are observation, interviews, questionnaires, schedules, and surveys.

3. What are data collection tools?

The term "data collecting tools" refers to the tools/devices used to gather data, such as a paper questionnaire or a system for computer-assisted interviews. Tools used to gather data include case studies, checklists, interviews, occasionally observation, surveys, and questionnaires.

4. What’s the difference between quantitative and qualitative methods?

While qualitative research focuses on words and meanings, quantitative research deals with figures and statistics. You can systematically measure variables and test hypotheses using quantitative methods. You can delve deeper into ideas and experiences using qualitative methodologies.

5. What are quantitative data collection methods?

While there are numerous other ways to get quantitative information, the methods indicated above—probability sampling, interviews, questionnaire observation, and document review—are the most typical and frequently employed, whether collecting information offline or online.

6. What is mixed methods research?

User research that includes both qualitative and quantitative techniques is known as mixed methods research. For deeper user insights, mixed methods research combines insightful user data with useful statistics.

7. What are the benefits of collecting data?

Collecting data offers several benefits, including:

• Knowledge and Insight
• Evidence-Based Decision Making
• Problem Identification and Solution
• Validation and Evaluation
• Identifying Trends and Predictions
• Support for Research and Development
• Policy Development
• Quality Improvement
• Personalization and Targeting
• Knowledge Sharing and Collaboration

8. What’s the difference between reliability and validity?

Reliability is about consistency and stability, while validity is about accuracy and appropriateness. Reliability focuses on the consistency of results, while validity focuses on whether the results are actually measuring what they are intended to measure. Both reliability and validity are crucial considerations in research to ensure the trustworthiness and meaningfulness of the collected data and measurements.

9. What is the role of data collection?

Data collection is an essential and imperative aspect for conducting any kind of research or analysis. It provides useful information which can then be used to help decision making and problem solving. In the absence of data collection, people would have no data to form conclusions in trends or make decisions in strategies.

10. What is the main purpose of data?

To make sense of our surroundings and also appreciate the likely effects they can have on us is what data enables us to do. It is useful in developing, assessing and seeking to remedy a situation. The data is useful for researchers in trying to explain a certain phenomenon and in problem solving.

11. What industries rely heavily on data collection?

The spheres of banking, media, and entertainment, healthcare, education, and manufacturing, insurance, transportation, and the government all rely on data collection. All of them leverage data in their activities in order to improve processes, services, and decision making.

12. How does data collection benefit businesses?

Through data collection, companies can get to know the various customers deeply, understand their wants and preferences, and adjust their marketing accordingly. More effective and more targeted marketing tactics allow better acquisition of customers and their satisfaction leading to better business results.

13. What is the difference between qualitative and quantitative data?

Quantitative data is focused on numbers and measurable metrics, while qualitative data is about descriptions and interpretations. Quantitative data provides concrete figures, whereas qualitative data offers insights into experiences and opinions.

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Data Collection Methods

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

Secondary Data Collection Methods

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

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

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

Primary Data Collection Methods

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

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

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

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

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

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

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John Dudovskiy

Data collection in research: Your complete guide

Last updated

31 January 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

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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What Is Data Collection? A Guide for Aspiring Data Scientists

• Written by John Terra
• Updated on February 1, 2024

With billions of active Internet users worldwide, it is no surprise that we generate massive amounts of data daily. This makes it challenging for researchers to find the correct data, collect it, and evaluate it for eventual use. That’s why there are data collectors.

This article explains data collection, including why it’s needed, the methods, tools, challenges, best practices, and how you can better understand how to collect and analyze data through online data science training .

So, before we explore this, let’s establish a definition. What is data collection?

What is Data Collection?

It involves collecting and evaluating information or data from multiple sources to answer questions, find answers to research problems, evaluate outcomes, and forecast probabilities and trends. It plays a considerable role in many types of analysis, research and decision-making, including in the social sciences, business, and healthcare.

Collecting data accurately is vital for making informed business decisions, ensuring quality assurance and maintaining research integrity.

During the data collection process, researchers must identify the different data types, sources of data, and methods being employed since there are many different methods to collect data for analysis. Many fields, including commercial, government and research, rely heavily on data collection.

But before an analyst starts collecting data, they must first answer three questions:

• What’s the goal or purpose of the research?
• What sorts of data were they planning on gathering?
• What procedures and methods will be used to collect, store, and process this information?

In addition, we can divide data into qualitative and quantitative categories. Qualitative data includes descriptions such as color, quality, size and appearance. As the name implies, quantitative data covers numbers, such as poll numbers, statistics, measurements, percentages, etc.

Also Read: Why Use Python for Data Science?

Why Do We Need Data Collection?

Informed decisions are the best decisions you can make. The more information you have, the more insightful your courses of action and the better chance of success. Today’s highly competitive commercial world demands that every enterprise that wants to not only stay afloat but thrive must make as few mistakes as possible.

Data collection helps organizations manage the sheer volumes of big data information and turn it into actionable insights that could prove to be a difference-maker.

So, what are the five methods of collecting data?

Presenting the Five Methods of Collecting Data

There’s a lot of data out there. Fortunately, there are many different types of data collection methods available to choose from. Let’s look into the five most popular methods of collecting data. Although there are additional methods, most industries and sectors rely extensively on these particular five methods.

• Direct observation. The researcher assumes the passive observer role, taking note of the subject’s behavior, words, and actions.
• Documents and records. This method involves conducting basic research on the topic in question and seeing what has been learned from past methods.
• Focus groups. Focus groups are essentially mass interviews. You can tailor group composition to fit a particular demographic.
• Interviews. One-on-one interviews allow researchers to collect data directly from personal communication with the subject.
• Surveys, quizzes, and questionnaires. This includes close-ended surveys, open-ended surveys, online questionnaires and quizzes.

Now, let’s look at the steps involved in a typical data collection procedure.

Also Read: A Beginner’s Guide to the Data Science Process

All About the Data Collection Process

It can be broken down into five steps. There’s symmetry here. Here are the steps involved in your standard data collection procedure:

• Figure out what data you want to collect. You begin the process by deciding what information you want to gather. Pick the subjects the data will cover, the sources used to gather it, and the information needed. For example, gathering information on products customers aged 20-40 searched for.
• Establish a deadline. Set a deadline at the outset of the planning phase. Although some forms of data may require perpetual collection, tracking the data throughout a given time frame is essential, especially if it’s for a particular campaign.
• Choose an approach. Select the data technique that will function as your foundation of the data gathering plan. Consider the kind of information you want to gather, the period during which you will receive the data, and any other factors involved.
• Gather the information. Once the plan is complete, implement the plan and start gathering data. Store and arrange our data, following the plan and monitoring its progress.
• Examine the information and apply your findings. At last, it’s time to examine the data and arrange the findings. The analysis stage is critical because it changes unprocessed data into insightful, applicable knowledge that benefits product design, marketing plans and business judgments.

The Significance of Guaranteeing Precise and Suitable Data Gathering

Your research insights will only be as good as the data-gathering attempt. You must use the correct data-gathering tools, focus on the right groups, and maintain research accuracy and integrity. If you don’t engage in research correctly, you may experience:

• Inaccurate conclusions that waste the organization’s resources
• Decisions that compromise the organization’s public policy
• Losing the capacity to respond to research inquiries correctly
• Causing actual harm to participants
• Misleading other researchers into adopting useless research avenues
• The inability to replicate and validate the findings makes it difficult to prove your findings

Also Read: What Is Data Mining? A Beginner’s Guide

Common Challenges Found While Collecting Data

As you may expect, data collection can be a daunting task. However, forewarned is forearmed, so here’s a list of the typical challenges that data collectors face.

Inconsistent Data

When you work with vastly different data sources, discrepancies may arise. The differences could be with formats, units or even spellings. Inconsistent data might also happen during corporate mergers or relocations. Unfortunately, data inconsistencies accumulate and reduce the data’s overall value if these issues aren’t resolved.

Ambiguous Data

Even if you have implemented strong oversight, some errors can still happen in vast databases or data lakes. Spelling mistakes go unnoticed, formatting difficulties occur, and column heads might be inaccurately displayed. This vague data can cause many problems for reporting and analytics.

Deciding Which Data to Collect

Sometimes, too many choices present a challenge. Deciding what data to collect is one of the most essential factors governing data collection and should be one of the first considerations while collecting data. Researchers must select the subjects the data will cover, the sources used to gather it, and the information needed. Neglecting this issue could lead to duplication of effort, collecting irrelevant data or ruining the entire study.

Data Downtime

Data is critical for the decisions and operations of a data-driven business. However, short periods of inaccessibility or unreliability may result in poor analytical outcomes and customer complaints. Data engineers spend about 80% of their time updating, maintaining, and guaranteeing data integrity in the pipeline. Much of the data downtime stems from migration issues or schema modifications. Thus, data downtime must be continuously monitored and reduced via automation.

Overabundant Data

Alternately known as “too much of a good thing,” there is a risk of getting lost in the abundance of data when looking for information relevant to your analytical efforts. Data analysts, data scientists and business users devote much of their work to finding and organizing appropriate data. Other data quality problems escalate when data volume increases, especially when working with streaming data and large files or databases.

Dealing with Big Data

Big data describes massive data sets with more intricate and diversified structures, resulting in increased challenges in storing, analyzing and extracting methods. Big data’s data sets are so large that more than conventional data processing tools are required. The amount of data generated by the Internet, healthcare applications, social media sites, the Internet of Things, technological advancements and increasingly larger organizations is rapidly growing.

Duplicate Data

Local databases, streaming data and cloud data lakes are just a couple of the data sources that modern enterprises deal with. Such sources are likely to duplicate and overlap with each other often. For example, duplicate contact information can adversely affect the customer’s experience. Additionally, the chance of biased analytical outcomes increases when duplicate data is involved. It can also result in ruining machine learning models with biased training data.

Inaccurate Data

Data accuracy is vital for highly regulated businesses such as healthcare. Inaccurate information doesn’t give organizations an accurate picture of the situation and thus can’t be used to plan the ideal course of action. Personalized customer experiences and marketing strategies underperform if the data is inaccurate. Causes of data inaccuracies include data degradation, human error and data drift. Global data decay happens at a rate of about 3% per month. Data integrity can also be compromised while transferred between different systems, and data quality may deteriorate over time.

Hidden Data

Most businesses only utilize a fraction of their data, with the rest often lost in data silos or exiled to data graveyards. Hidden data reduces the chances of developing exciting new products, improves service and streamlines organizational procedures.

Finding the Relevant Data

Finding relevant data isn’t always easy. There are several circumstances that we need to account for while trying to find relevant data, including:

• Relevant domain
• Relevant demographics
• Relevant time

Irrelevant data in any factor renders it obsolete and unsuitable for analysis. This may lead to incomplete research or analysis, multiple repetitive attempts or the halt of the study.

Low Response and Poor Design

Finally, poor design and low response rates occur during the data collection process, especially in health surveys that use questionnaires. These factors may lead to insufficient or inadequate data supplies for the study. Creating an incentivized program could mitigate these issues and generate more responses.

So, how do we handle this formidable list of challenges? By instituting best practices, of course!

Key Considerations and Best Practices

Here are some of data collection’s best practices that can lead to better results:

Carefully Consider What Data to Collect

It’s too easy to get data about anything and everything, but it’s critical only to collect the required information. Consider these three questions:

• What specific details do you need?
• What details are available?
• What details will be most useful?

Plan How to Collect Each Data Point

There is a lack of freely accessible data. Consider how much time and effort gathering each piece of information requires as you decide what data to acquire.

Consider the Price of Each Extra Data Point

Once you decide what data to gather, factor in the expense. Surveyors and respondents incur additional costs for every extra data point or survey question.

Consider Available Data Collection Options from Mobile Devices

Mobile-based data collecting can be split into three distinct categories:

• Field surveyors. Thanks to smartphone apps, these surveyors directly enter data into interactive questionnaires while speaking to each respondent.
• IVRS (interactive voice response technology). This method calls potential respondents and asks them pre-recorded questions.
• SMS. This method sends a text message containing questions to the customer, who can then respond by text on their smartphone.

And while we’re talking about mobile devices…

• Data collection via mobile devices is a big thing. Modern technology is increasingly relying on mobile devices. Collecting data from mobile devices is an easy, cost-effective tactic.
• Don’t forget identifiers. Identifiers, or details that describe the source and context of a survey response, are just as important as the program or subject information being researched. Adding more identifiers lets you pinpoint the program’s successes and failures with greater accuracy.

Also Read: Career Guide: How to Become a Data Engineer

Do You Want to Become a Data Scientist?

If you want to become a data scientist or just collect those skills, check out this 44-week data science bootcamp . You will learn the essential data science, machine learning, and analytical skills needed for a solid career in the field.

Glassdoor.com shows that data scientists in the United States make an average yearly salary of $129,127. So, check out the bootcamp and enhance your critical data science skills!

Q: What do you mean by data collection? A: It is the act of collecting and evaluating information or data from many sources to answer questions, find answers to research problems, evaluate outcomes, and forecast probabilities and trends.

Q: What are the five methods of collecting data? A: The five data collection methods are:

• Direct observation
• Documents and records
• Focus groups
• Surveys, quizzes, and questionnaires

Q: What are the benefits of data collection? A: The benefits include:

• Knowledge sharing and collaboration
• Policy development
• Evidence-based decision making
• Problem identification and solutions
• Validation and evaluation
• Personalization and targeting
• Identifying trends and predictions
• Support for research and development
• Quality improvement

You might also like to read:

Data Collection Methods: A Comprehensive View

What Is Data Processing? Definition, Examples, Trends

Differences Between Data Scientist and Data Analyst: Complete Explanation

A Data Scientist Job Description: The Roles and Responsibilities in 2024

What Is Data? A Beginner’s Guide

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What Is Data Collection: Definition, Methods, Techniques & Examples

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Data collection is the process of gathering and measuring information on specific topics. Data can be collected from multiple sources including surveys, focus groups, interviews, questionnaires, observations, and existing databases. The gathered information may then be organized into tables or charts for further analysis. The goal of data collection is to retrieve information that can be utilized to recognize patterns and optimize processes.

In this guide, we will define what data collection is and outline key data collection methods any researcher needs to be familiar with. This article will guide you through critical tools and techniques for this process. Also, we have tremendous and helpful examples for this part of the research. No doubt that you will be advanced in research after going deeper into methods of gathering data with our experts!

Once you collect all the information, be prepared to analyze data and interpret it in your study. But if you don’t want to deal with writing, buy a research paper from our experts and enjoy top-quality results.

What Is Data Collection: Definition

Before we jump into tips and methods of research and gathering information, let’s define a data collection term. In a nutshell, before you will make a business decision, answer a research question or test your hypothesis , you need collected data. It is the information to analyze and outline answers to your questions. 

There are a lot of approaches to gathering information . They depend on the project's purpose and aims. For example, for academics, this process will depend on a type of research methodology — will they use qualitative or quantitative analysis, and what field are they working in? Next, we will focus on how to know what information you need to collect for valuable analysis.

Questions to Ask Before You Collect Data

The most important question before collecting data is to outline what exactly you need to gather to answer your research question or business analytic needs. It may look relatively easy, but before launching this process, ask yourself those questions:

• What is my research aim and goal?
• What type of info do I need to answer research questions?
• What analysis will I apply to this outcome?
• How will I store and manage collected information?
• How to ensure accuracy when I gather data?

Why Collecting Data Is Important?

You may wonder why we pay so much attention to data collection as valuable insights for our research we will get from an analysis process. However, you won’t be able to conduct a proper analysis if you collect data that is irrelevant or unclear. Your whole research project can fail because your data gathering techniques were compromised. 

Here are a few reasons why this process is important and why you need to consider the best ways to collect data:

• Accurate results Choosing the right methods is critical to obtaining accurate results and meaningful insights. Incorrect methods can lead to wrong data and inaccurate conclusions.
• Clear analysis Effective data collection helps define the analysis process and ensures that research questions are answered fully and coherently.
• Informed decision-making Gathering the right data is essential for making informed decisions and making the best choices.
• Preventing errors Mistakes could lead to wrong predictions or misinterpretations.
• Problem-solving A properly planned strategy helps identify the optimal directions to solve issues outlined in the problem statement .

Hopefully, you already have an understanding of why it is so essential to go deeper into gathering information. In the following paragraphs, we will guide you through methodology and techniques to help you choose one that fits your research.

Types of Data Collection Methods

As you already may know, there are different types of data collection in research. The whole process will depend on a set of variables, project goals, and questions you want to answer in this work. 

First and foremost, there are two main types of information:

• Primary Primary information is first-hand data collected by the researcher that was not analyzed before.
• Secondary Secondary collection of data is organized by a third party and has already been analyzed for other purposes.

Second, you need to define methods of data collection in research. Two key types of collected information for the research are listed below.

• Quantitative data Quantitative information involves numbers that can be used for statistical analysis .
• Qualitative data Qualitative data includes transcript of an interview, focus group, or in other words – everything that is related to letters instead of numbers.

Let’s delve deeper into each type of data you may need for testing hypotheses or answering research questions.

Primary Data Collection Methods

Let’s start with the most frequently used type of information gathering for any analytical work. Primary data collection methods in research focus on raw outcomes you may extract or collect. It can be both qualitative or quantitative info, but a crucial element is that this information was not analyzed before by other researchers. 

Here is how you can collect information for conducting this type of analysis. It can be:

• Survey research
• Projective data gathering
• Focus group
• Questionnaires
• Observation

Ethnography

• Delphi data collection technique.

Next, we will delineate the specifics of each method.

These research data collection methods are used in case you need to find general characteristics or opinions on something. You can use it for both types of research. For example, run a correlation for some questions and content analysis for others. Surveys can be done online, by phone, or in person. However, it can also be a way to distribute data by formulating the survey questions.

This data collection method will help to go deeper into the understanding of a topic or issue. This is a one-to-one conversation based on questions you derive from a theoretical ground. Researchers use interviews for a qualitative study when they need to discuss some issues. It must be open-end questions to ensure that the recipient will go deeper into the topic. Interviews can also be online or offline, the responses should be recorded and transcribed later.

Projective Data Gathering

This data gathering method allows respondents to project their opinion or subjective beliefs on other people. Then this information will help researchers to understand real behavioral reasons better. It can be used during your interviews in small groups. Often you will find that political researchers use it. For example, if people do not want to say how they voted, they can be asked if they can guess how their neighbors voted. Research responses still will tell more about responders than about their neighbors.

Focus Groups

In this case, researchers conduct a discussion in a small group to collect data and analyze it later. Those 5-7 people can be representatives of one or different social groups. Researchers ask questions and can determine how answers from other people affect each one in a group. It helps to understand the issue better and get some insights on the topic. However, a list of questions for your focus group should be defined previously.

One of the data collection strategies is to get a set of straightforward answers to simple questions. It can be structured questionnaires for quantitative research or unstructured for qualitative. Also, there can be various types of questions — open-ended, yes/no, multiple choice, and others. The aim of this type of gathering information is to have as much information about responders as possible.

This method of data collection can be used to research something in natural circumstances without affecting a situation. In other words, researchers can observe the behavior of someone in a specific situation without mentioning that this is research. However, the data should be collected or noted through surveys or journals. All details should be carefully fixed for future analysis.

This way of collecting data involves testing hypotheses to get the information for analysis. Researchers usually manipulate variables and measure their effect on each other. In other words, to gather the information, you need to launch a few tests and then use the results for analytics. This method can be applied to test hypotheses. You can understand how the variables can change any situation.

This is quite a popular technique to gather data. This method means the observation of a community, culture, or group of people first-handed. It is often applied to expeditions for learning the cultural or social specifics of the researched group. You must record all the observations (audio or text) and also add some reflection that will help to understand the phenomenon better.

Delphi Data CollectionTechnique

This data collection method is most frequently used in economic research to gather expert opinions on research questions and get a consensus on them. In this case, the question is asked of a group of experts, and as a result, they provide a consolidated opinion on the topic. The aim is to collect an expert judgment on an issue that can open a new perspective on understanding and researching the topic.

Secondary Methods of Data Collection

As opposed to primary data collection methods, there are no specific ways of gathering the information in case of secondary data. All the data are already collected by other researchers. For example, someone researched the influence of metals on soils and already has test results. If you are also working on this topic, you can use the data collected by others to run your own analysis. (For instance, look at some correlations that were not previously observed.) 

As there are no techniques for secondary info gathering, here are some sources that can be used for further analysis:

• Sales reports
• Business statements
• Government reports
• Customer personal information, etc.

Research Data Collection Tools

In this article, we already discussed why we should collect data and what method to use in each situation. However, it is also essential to be aware of the best data collection tools. In other words, you need to understand what you may use for accurate information gathering. Let’s briefly discuss a popular data collection tool you may apply in your research. 

• Online survey One of the most frequently used types of survey. It allows researchers to find a lot of responders. You may use some paid (like Qualtrics ) or free versions (like Goggle forms ) to construct surveys. However, be careful with possible fake responders.
• Checklists You may use it while speaking with a responder — printed or online version.
• Role-Playing With this data collection tool, responders pretend to be in a specific imaginary situation to answer your questions.
• Offline survey You may also use the old-school tool of in-person surveys. It means asking people to respond to your questions and marking the answers in your printed forms.
• Case studies Case study allows us to research variables in a specific situation and analyze the influence of circumstances.

Data Collection Examples

To be more specific with all the research methodology and tools we outline in this text, let's look at examples of data collection. It will definitely help to understand what technique you need to apply to each situation and research case.

Data collection example 1

Let’s pretend you are looking at how bots and trolls in social media influence public opinion about the presidential election. To understand it, you need to measure how each of the fake news shared and pushed by bots change people’s view on the political situation. To analyze it, you may run a survey with a questionnaire you developed based on the theoretical ground. After you have your responses, you can use descriptive statistics or content analysis to get insights from this information.

Data collection example 2

You are going on an expedition and want to write an academic paper on wedding traditions in tribes. Using the ethnographic techniques, you will observe the actual wedding, make notes, notice some personal reflections, and write it down in the journal. After you start writing the text, you will use these notes as a base for your research.

Why Is It Important to Use Accurate and Appropriate Data Collection Techniques?

To answer this question, you need to imagine how the wrong data can influence the whole research. An accurate data collection procedure is a guarantee that your paper or analytical work will bring valuable and practical insights. For instance, the decision-making should often rely on the results section of your analyses, and incorrect data collecting will ruin the whole study. It will cause wrong predictions and will manipulate the final conclusion section. 

Why may you be wrong with gathering the info for analysis?

• Choosing wrong collection methods.
• Do not understanding the aims of your project.
• You did not consider the limitations of the study.
• You choose the wrong tool that does not work with a specific type of data
• Methodology can’t help to answer your research question.

You definitely don’t want to be a researcher whose studies do not work and never help to solve a practical problem. That is why academics need to put maximum effort into the accuracy of gathering information before analysis.

How to Collect Data for Research Step-By-Step

If you are already afraid to start the research, we got a guide with detailed steps in data collection process. In most grad schools, students have a few classes on research and usually learn what method or technique can be used for their work. It is not rocket science to complete accurate and valuable research. You can use our advice for your concrete case. Let’s discuss each step in detail! 

Note that collecting data takes time. The more details you gather, the longer it takes. That’s why we suggest collecting information on a  ratio level .

1. Determine the Goal of Your Research

Before you start to collect and analyze data, you need to define critical questions. Why do you conducting this research, and what do you want to achieve? You may think that it's a piece of cake and there is no need to spend a lot of time on this step. However, we would say that you can start to gather data only when you understand why and for what purpose. What does it mean? 

First, your research goal will determine data collection techniques. For instance, you can answer research questions with qualitative data and test hypotheses with quantitative research. Second, accuracy with a final goal will help to define what type of information you need for analysis.

2. Choose a Data Collection Strategy

After you are clear with the research aims, you need to choose the strategy to collect data. It can be an experiment or survey, ethnographic method, or focus group. The gathering strategy should fit the analytical planning. In other words, you need to understand what technology and tools will help you to get the information you need for your research design . 

For instance, you are looking for the influence of social media on vaccination information campaigns. In this case, you will need to decide on a referral group and launch the survey. That is how you will measure the theory of change and role of social media strategies for this kind of campaign.

3. Plan Your Data Collection Process

Outline goals of your paper and define the strategy for collecting data for research. What’s next? We would recommend focusing on detailed planning of data collection procedures for your work. This process can take some time and can be divided into a few phases.

• Define your dependent and independent variables. Determine independent and dependent variables to see the relationship between them. Maybe you need to measure variables that can’t be directly observed, and you will need to design a survey. In other cases, you will need to access data without interaction with responders, like age or place of living.
• Design your sampling. In case you run a survey, interview, questionnaire, or focus group, you need to outline proper questions that will lead you to future valuable analysis. All questions should derive from the theory you are using for this research.
• Delineate data management plan. Researchers need to plan how they will store data. It can be a transcript of the interview (paper form or voice recording), video, or audio for the focus groups. The way to manage and save data will rely on your methodology.

4. Collect Data

The final step is to collect data – implement a tactic and strategy you defined before. It is essential to be accurate and follow all steps in gathering data. You can check various examples of this research stage. 

For instance, researchers can collect tweets for analysis using R or Python code and then convert them into Excel for further analysis. Or you can have interviews with experts, recode the audio, transcribe it, and then code as a part of the content analysis methodology. 

The other example is launching an online survey. You will need to send links to people you want to get responses from. If the researcher uses the automated tool, it is possible to get the whole information in tables or convert it into the form you need

Be sure that all the numbers you have are reliable and validated. This is the core of valuable outcomes.

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5. Analyze and Interpret Gathered Data

The last step you will conduct is analyzing collected data to outline insights. After you have an excel file or transcribed interview, you can apply the methodology to get results. Collecting and analyzing data processes should be planned together, as they are highly related. 

The analytical approach you use to obtain results depends on the type of outcomes you gather. For instance, if you conduct a survey, you might require measuring standard deviation or correlation for specific data points. Therefore, it is crucial to have clear research objectives to ensure successful work.

Data Collection Tips & Suggestion

We went through each question in data collecting very carefully, and we hope you are ready to launch your own research and ensure the quality of results. However, if you need just a short overview of best practices before you start to collect data, you are in the right place. Let’s look at the essential tips and tricks you may use for your practices!

• Be clear with data collection techniques for concrete research. It may happen that you do not understand the aim of the work, and then you will have a problem in the information analysis step.
• Ensure that your data collection strategies are in line with an analysis methodology. It can save you a bunch of time.
• Think about all limitations you may have. Gather information that will answer your questions or test your hypothesis.
• Be aware of pricing for adding additional information points into the research. A lot of tools for sampling gathering are chargeable, and you need to plan the whole research process first to avoid extra payments in the future.
• Have in mind the research goals all time. You may make a lot of mistakes in analysis, in case you change the research goal. Follow the one goal you determined at the beginning of your research.

Bottom Line on Data Collection

We are sure that after reading a whole text, you are ready to conduct valuable research! In this blog, our team explained what data collection in research is, how data is collected and recorded, and the best examples of collecting data. We prepared a detailed guide on methods and tools researchers can use in their work. You may wonder if all information is applicable to the different disciplines in academia, the same as in business decision-making analysis. The answer is yes. We delineate technologies that can be useful for each type of research. Just check our guide in case you still have questions.

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FAQ on Data Collecting

1. what are the 4 methods of data collection.

There are a few key types of data collection methods that can be applied to any type of work. First, primary or secondary collection. In other words, you can get raw data for analysis or work with a piece of information collected by a third-hand party. Also, there is qualitative information (analysis of words) and quantitative (number analysis).

2. What are data collection tools?

The research tools for data collection depend on the research goals and the type of information you gather. After you define what type of information you need for your analysis. We define such tools as: 

• Focus groups
• Ethnographic study
• Delphi data collection.

3. What type of data collection is most likely to be timely and expensive?

From the practical perspective, the data collection method may be more cost-consuming for the researchers. We are talking about surveys, as in some cases, you will need to run hundreds or thousands of them. It means you need to find relevant people and ensure their answers. However, for some disciplines, experiments are the most expensive ones.

4. What type of data collection methods has the lowest response rate?

Speaking about the method of data collection that will bring you the lowest response rate, it would definitely be an online survey. In most cases, because people use to have a lot of emails and can skip your request. Choose the tools for the survey with monitoring options.

5. What is the simplest way to collect data?

Probably, the simplest way is to run questionnaires. This is the easiest way to gather data. It is usually simple questions, yes/no type. You can collect the basic responses quite quickly and get a general opinion analysis. However, this method does not fit all types of research.

6. What are the challenges in data collection?

You can face a bunch of various issues while collecting data for the analysis. Here are the most common:

• Quality problems: poor quality of the extract or gathered information won’t be helpful.
• Ambiguous data: you can skip some errors if you are working with huge sets.
• Too much data: in many cases, you don’t need all data, only what you defined in your first strategy step.
• Hidden information: not all that you need for the research can be obtained easily. A lot of information has privacy protection.

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