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How to write a research plan: Step-by-step guide

Last updated

30 January 2024

Reviewed by

Today’s businesses and institutions rely on data and analytics to inform their product and service decisions. These metrics influence how organizations stay competitive and inspire innovation. However, gathering data and insights requires carefully constructed research, and every research project needs a roadmap. This is where a research plan comes into play.

There’s general research planning; then there’s an official, well-executed research plan. Whatever data-driven research project you’re gearing up for, the research plan will be your framework for execution. The plan should also be detailed and thorough, with a diligent set of criteria to formulate your research efforts. Not including these key elements in your plan can be just as harmful as having no plan at all.

Read this step-by-step guide for writing a detailed research plan that can apply to any project, whether it’s scientific, educational, or business-related.

• What is a research plan?

A research plan is a documented overview of a project in its entirety, from end to end. It details the research efforts, participants, and methods needed, along with any anticipated results. It also outlines the project’s goals and mission, creating layers of steps to achieve those goals within a specified timeline.

Without a research plan, you and your team are flying blind, potentially wasting time and resources to pursue research without structured guidance.

The principal investigator, or PI, is responsible for facilitating the research oversight. They will create the research plan and inform team members and stakeholders of every detail relating to the project. The PI will also use the research plan to inform decision-making throughout the project.

• Why do you need a research plan?

Create a research plan before starting any official research to maximize every effort in pursuing and collecting the research data. Crucially, the plan will model the activities needed at each phase of the research project.

Like any roadmap, a research plan serves as a valuable tool providing direction for those involved in the project—both internally and externally. It will keep you and your immediate team organized and task-focused while also providing necessary definitions and timelines so you can execute your project initiatives with full understanding and transparency.

External stakeholders appreciate a working research plan because it’s a great communication tool, documenting progress and changing dynamics as they arise. Any participants of your planned research sessions will be informed about the purpose of your study, while the exercises will be based on the key messaging outlined in the official plan.

Here are some of the benefits of creating a research plan document for every project:

Project organization and structure

Well-informed participants

All stakeholders and teams align in support of the project

Clearly defined project definitions and purposes

Distractions are eliminated, prioritizing task focus

Timely management of individual task schedules and roles

Costly reworks are avoided

• What should a research plan include?

The different aspects of your research plan will depend on the nature of the project. However, most official research plan documents will include the core elements below. Each aims to define the problem statement, devising an official plan for seeking a solution.

Specific project goals and individual objectives

Ideal strategies or methods for reaching those goals

Required resources

Descriptions of the target audience, sample sizes, demographics, and scopes

Key performance indicators (KPIs)

Project background

Research and testing support

Preliminary studies and progress reporting mechanisms

Cost estimates and change order processes

Depending on the research project’s size and scope, your research plan could be brief—perhaps only a few pages of documented plans. Alternatively, it could be a fully comprehensive report. Either way, it’s an essential first step in dictating your project’s facilitation in the most efficient and effective way.

• How to write a research plan for your project

When you start writing your research plan, aim to be detailed about each step, requirement, and idea. The more time you spend curating your research plan, the more precise your research execution efforts will be.

Account for every potential scenario, and be sure to address each and every aspect of the research.

Consider following this flow to develop a great research plan for your project:

Define your project’s purpose

Start by defining your project’s purpose. Identify what your project aims to accomplish and what you are researching. Remember to use clear language.

Thinking about the project’s purpose will help you set realistic goals and inform how you divide tasks and assign responsibilities. These individual tasks will be your stepping stones to reach your overarching goal.

Additionally, you’ll want to identify the specific problem, the usability metrics needed, and the intended solutions.

Know the following three things about your project’s purpose before you outline anything else:

What you’re doing

Why you’re doing it

What you expect from it

Identify individual objectives

With your overarching project objectives in place, you can identify any individual goals or steps needed to reach those objectives. Break them down into phases or steps. You can work backward from the project goal and identify every process required to facilitate it.

Be mindful to identify each unique task so that you can assign responsibilities to various team members. At this point in your research plan development, you’ll also want to assign priority to those smaller, more manageable steps and phases that require more immediate or dedicated attention.

Select research methods

Research methods might include any of the following:

User interviews: this is a qualitative research method where researchers engage with participants in one-on-one or group conversations. The aim is to gather insights into their experiences, preferences, and opinions to uncover patterns, trends, and data.

Field studies: this approach allows for a contextual understanding of behaviors, interactions, and processes in real-world settings. It involves the researcher immersing themselves in the field, conducting observations, interviews, or experiments to gather in-depth insights.

Card sorting: participants categorize information by sorting content cards into groups based on their perceived similarities. You might use this process to gain insights into participants’ mental models and preferences when navigating or organizing information on websites, apps, or other systems.

Focus groups: use organized discussions among select groups of participants to provide relevant views and experiences about a particular topic.

Diary studies: ask participants to record their experiences, thoughts, and activities in a diary over a specified period. This method provides a deeper understanding of user experiences, uncovers patterns, and identifies areas for improvement.

Five-second testing: participants are shown a design, such as a web page or interface, for just five seconds. They then answer questions about their initial impressions and recall, allowing you to evaluate the design’s effectiveness.

Surveys: get feedback from participant groups with structured surveys. You can use online forms, telephone interviews, or paper questionnaires to reveal trends, patterns, and correlations.

Tree testing: tree testing involves researching web assets through the lens of findability and navigability. Participants are given a textual representation of the site’s hierarchy (the “tree”) and asked to locate specific information or complete tasks by selecting paths.

Usability testing: ask participants to interact with a product, website, or application to evaluate its ease of use. This method enables you to uncover areas for improvement in digital key feature functionality by observing participants using the product.

Live website testing: research and collect analytics that outlines the design, usability, and performance efficiencies of a website in real time.

There are no limits to the number of research methods you could use within your project. Just make sure your research methods help you determine the following:

What do you plan to do with the research findings?

What decisions will this research inform? How can your stakeholders leverage the research data and results?

Recruit participants and allocate tasks

Next, identify the participants needed to complete the research and the resources required to complete the tasks. Different people will be proficient at different tasks, and having a task allocation plan will allow everything to run smoothly.

Prepare a thorough project summary

Every well-designed research plan will feature a project summary. This official summary will guide your research alongside its communications or messaging. You’ll use the summary while recruiting participants and during stakeholder meetings. It can also be useful when conducting field studies.

Ensure this summary includes all the elements of your research project. Separate the steps into an easily explainable piece of text that includes the following:

An introduction: the message you’ll deliver to participants about the interview, pre-planned questioning, and testing tasks.

Interview questions: prepare questions you intend to ask participants as part of your research study, guiding the sessions from start to finish.

An exit message: draft messaging your teams will use to conclude testing or survey sessions. These should include the next steps and express gratitude for the participant’s time.

Create a realistic timeline

While your project might already have a deadline or a results timeline in place, you’ll need to consider the time needed to execute it effectively.

Realistically outline the time needed to properly execute each supporting phase of research and implementation. And, as you evaluate the necessary schedules, be sure to include additional time for achieving each milestone in case any changes or unexpected delays arise.

For this part of your research plan, you might find it helpful to create visuals to ensure your research team and stakeholders fully understand the information.

Determine how to present your results

A research plan must also describe how you intend to present your results. Depending on the nature of your project and its goals, you might dedicate one team member (the PI) or assume responsibility for communicating the findings yourself.

In this part of the research plan, you’ll articulate how you’ll share the results. Detail any materials you’ll use, such as:

Presentations and slides

A project report booklet

A project findings pamphlet

Documents with key takeaways and statistics

Graphic visuals to support your findings

• Format your research plan

As you create your research plan, you can enjoy a little creative freedom. A plan can assume many forms, so format it how you see fit. Determine the best layout based on your specific project, intended communications, and the preferences of your teams and stakeholders.

Find format inspiration among the following layouts:

Written outlines

Narrative storytelling

Visual mapping

Graphic timelines

Remember, the research plan format you choose will be subject to change and adaptation as your research and findings unfold. However, your final format should ideally outline questions, problems, opportunities, and expectations.

• Research plan example

Imagine you’ve been tasked with finding out how to get more customers to order takeout from an online food delivery platform. The goal is to improve satisfaction and retain existing customers. You set out to discover why more people aren’t ordering and what it is they do want to order or experience. 

You identify the need for a research project that helps you understand what drives customer loyalty. But before you jump in and start calling past customers, you need to develop a research plan—the roadmap that provides focus, clarity, and realistic details to the project.

Here’s an example outline of a research plan you might put together:

Project title

Project members involved in the research plan

Purpose of the project (provide a summary of the research plan’s intent)

Objective 1 (provide a short description for each objective)

Objective 2

Objective 3

Proposed timeline

Audience (detail the group you want to research, such as customers or non-customers)

Budget (how much you think it might cost to do the research)

Risk factors/contingencies (any potential risk factors that may impact the project’s success)

Remember, your research plan doesn’t have to reinvent the wheel—it just needs to fit your project’s unique needs and aims.

Customizing a research plan template

Some companies offer research plan templates to help get you started. However, it may make more sense to develop your own customized plan template. Be sure to include the core elements of a great research plan with your template layout, including the following:

Introductions to participants and stakeholders

Background problems and needs statement

Significance, ethics, and purpose

Research methods, questions, and designs

Preliminary beliefs and expectations

Implications and intended outcomes

Realistic timelines for each phase

Conclusion and presentations

How many pages should a research plan be?

Generally, a research plan can vary in length between 500 to 1,500 words. This is roughly three pages of content. More substantial projects will be 2,000 to 3,500 words, taking up four to seven pages of planning documents.

What is the difference between a research plan and a research proposal?

A research plan is a roadmap to success for research teams. A research proposal, on the other hand, is a dissertation aimed at convincing or earning the support of others. Both are relevant in creating a guide to follow to complete a project goal.

What are the seven steps to developing a research plan?

While each research project is different, it’s best to follow these seven general steps to create your research plan:

Defining the problem

Identifying goals

Choosing research methods

Recruiting participants

Preparing the brief or summary

Establishing task timelines

Defining how you will present the findings

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The Essential Guide to Doing Your Research Project

Student resources, chapter 7: designing a research plan.

A.    Checklist for Assessing Practicality

By running through the following questions, you can quickly assess the practicality of your methodological plan:

1.    Do you have/can you develop necessary expertise?

Interviewing, observing, theorizing, surveying, statistical analysis – various methods of data collection and analysis will require certain skills. And while you can develop new skills, time / interest can be an issue. Remember - competence is not a luxury. Your skills or lack thereof, will affect the quality of the data you collect and the credibility of the findings you generate.

2.    Is your method ethical? Is it likely to get required ethics approval?

A clear criterion of any research design is that it is ethical; and ethicality is likely to be audited by an ethics committee. If a study calls for interaction with people, it will often require formal workplace and/ or university ethics committee approval. Ethical studies take responsibility for integrity in the production of knowledge and ensures that the mental, emotional, and physical welfare of respondents is protected.

3.    Do you have required access to data?

A major challenge for researchers is gaining access to data. Whether you plan to explore documents, conduct interviews or surveys, or engage in observation, the best-laid plans are worthless if you can’t find a way to access people, places and/ or records.

4.    Is your time frame realistic?

If you have not given yourself long enough to do what your design demands, you are likely to: miss deadlines; compromise your study by changing your methods mid-stream; do a shoddy job with your original methods; compromise time that should be dedicated to other aspects of your job/ life; or finally, not completing your study at all.

5.    Do you have required financial/organizational support?

Whether you need to cover the cost of materials, postage, transcription etc., or the cost of bringing in a professional researcher to help with data collection or analysis, you will need finances. It is important to develop a realistic budget for your study. Research into any problem, no matter how worthy, will not be practical, or in fact, possible if you can’t cover costs. Also make sure that, if appropriate, you have organizational support for time to be dedicated to your project. Not being able to find time can be as debilitating to your study as not being able to find money.

B.    Checklist for Fundamental Methods Questions

-  Who do you want to be able to speak about?

-  Who do you plan to speak to/observe?

-  What is the physical domain of your sample?

-  Are settings relevant to the credibility of your methods?

-  How do your methods fit into your time frame?

-  Is timing relevant to the credibility of your methods?

-  How will I collect my data?

-  How will I implement my methods?

-  What will you look for/what will you ask?

6.3 Steps in a Successful Marketing Research Plan

Learning outcomes.

By the end of this section, you will be able to:

• 1 Identify and describe the steps in a marketing research plan.
• 2 Discuss the different types of data research.
• 3 Explain how data is analyzed.
• 4 Discuss the importance of effective research reports.

Define the Problem

There are seven steps to a successful marketing research project (see Figure 6.3 ). Each step will be explained as we investigate how a marketing research project is conducted.

The first step, defining the problem, is often a realization that more information is needed in order to make a data-driven decision. Problem definition is the realization that there is an issue that needs to be addressed. An entrepreneur may be interested in opening a small business but must first define the problem that is to be investigated. A marketing research problem in this example is to discover the needs of the community and also to identify a potentially successful business venture.

Many times, researchers define a research question or objectives in this first step. Objectives of this research study could include: identify a new business that would be successful in the community in question, determine the size and composition of a target market for the business venture, and collect any relevant primary and secondary data that would support such a venture. At this point, the definition of the problem may be “Why are cat owners not buying our new cat toy subscription service?”

Additionally, during this first step we would want to investigate our target population for research. This is similar to a target market, as it is the group that comprises the population of interest for the study. In order to have a successful research outcome, the researcher should start with an understanding of the problem in the current situational environment.

Develop the Research Plan

Step two is to develop the research plan. What type of research is necessary to meet the established objectives of the first step? How will this data be collected? Additionally, what is the time frame of the research and budget to consider? If you must have information in the next week, a different plan would be implemented than in a situation where several months were allowed. These are issues that a researcher should address in order to meet the needs identified.

Research is often classified as coming from one of two types of data: primary and secondary. Primary data is unique information that is collected by the specific researcher with the current project in mind. This type of research doesn’t currently exist until it is pulled together for the project. Examples of primary data collection include survey, observation, experiment, or focus group data that is gathered for the current project.

Secondary data is any research that was completed for another purpose but can be used to help inform the research process. Secondary data comes in many forms and includes census data, journal articles, previously collected survey or focus group data of related topics, and compiled company data. Secondary data may be internal, such as the company’s sales records for a previous quarter, or external, such as an industry report of all related product sales. Syndicated data , a type of external secondary data, is available through subscription services and is utilized by many marketers. As you can see in Table 6.1 , primary and secondary data features are often opposite—the positive aspects of primary data are the negative side of secondary data.

There are four research types that can be used: exploratory, descriptive, experimental, and ethnographic research designs (see Figure 6.4 ). Each type has specific formats of data that can be collected. Qualitative research can be shared through words, descriptions, and open-ended comments. Qualitative data gives context but cannot be reduced to a statistic. Qualitative data examples are categorical and include case studies, diary accounts, interviews, focus groups, and open-ended surveys. By comparison, quantitative data is data that can be reduced to number of responses. The number of responses to each answer on a multiple-choice question is quantitative data. Quantitative data is numerical and includes things like age, income, group size, and height.

Exploratory research is usually used when additional general information in desired about a topic. When in the initial steps of a new project, understanding the landscape is essential, so exploratory research helps the researcher to learn more about the general nature of the industry. Exploratory research can be collected through focus groups, interviews, and review of secondary data. When examining an exploratory research design, the best use is when your company hopes to collect data that is generally qualitative in nature. 7

For instance, if a company is considering a new service for registered users but is not quite sure how well the new service will be received or wants to gain clarity of exactly how customers may use a future service, the company can host a focus group. Focus groups and interviews will be examined later in the chapter. The insights collected during the focus group can assist the company when designing the service, help to inform promotional campaign options, and verify that the service is going to be a viable option for the company.

Descriptive research design takes a bigger step into collection of data through primary research complemented by secondary data. Descriptive research helps explain the market situation and define an “opinion, attitude, or behavior” of a group of consumers, employees, or other interested groups. 8 The most common method of deploying a descriptive research design is through the use of a survey. Several types of surveys will be defined later in this chapter. Descriptive data is quantitative in nature, meaning the data can be distilled into a statistic, such as in a table or chart.

Again, descriptive data is helpful in explaining the current situation. In the opening example of LEGO , the company wanted to describe the situation regarding children’s use of its product. In order to gather a large group of opinions, a survey was created. The data that was collected through this survey allowed the company to measure the existing perceptions of parents so that alterations could be made to future plans for the company.

Experimental research , also known as causal research , helps to define a cause-and-effect relationship between two or more factors. This type of research goes beyond a correlation to determine which feature caused the reaction. Researchers generally use some type of experimental design to determine a causal relationship. An example is A/B testing, a situation where one group of research participants, group A, is exposed to one treatment and then compared to the group B participants, who experience a different situation. An example might be showing two different television commercials to a panel of consumers and then measuring the difference in perception of the product. Another example would be to have two separate packaging options available in different markets. This research would answer the question “Does one design sell better than the other?” Comparing that to the sales in each market would be part of a causal research study. 9

The final method of collecting data is through an ethnographic design. Ethnographic research is conducted in the field by watching people interact in their natural environment. For marketing research, ethnographic designs help to identify how a product is used, what actions are included in a selection, or how the consumer interacts with the product. 10

Examples of ethnographic research would be to observe how a consumer uses a particular product, such as baking soda. Although many people buy baking soda, its uses are vast. So are they using it as a refrigerator deodorizer, a toothpaste, to polish a belt buckle, or to use in baking a cake?

Select the Data Collection Method

Data collection is the systematic gathering of information that addresses the identified problem. What is the best method to do that? Picking the right method of collecting data requires that the researcher understand the target population and the design picked in the previous step. There is no perfect method; each method has both advantages and disadvantages, so it’s essential that the researcher understand the target population of the research and the research objectives in order to pick the best option.

Sometimes the data desired is best collected by watching the actions of consumers. For instance, how many cars pass a specific billboard in a day? What website led a potential customer to the company’s website? When are consumers most likely to use the snack vending machines at work? What time of day has the highest traffic on a social media post? What is the most streamed television program this week? Observational research is the collecting of data based on actions taken by those observed. Many data observations do not require the researched individuals to participate in the data collection effort to be highly valuable. Some observation requires an individual to watch and record the activities of the target population through personal observations .

Unobtrusive observation happens when those being observed aren’t aware that they are being watched. An example of an unobtrusive observation would be to watch how shoppers interact with a new stuffed animal display by using a one-way mirror. Marketers can identify which products were handled more often while also determining which were ignored.

Other methods can use technology to collect the data instead. Instances of mechanical observation include the use of vehicle recorders, which count the number of vehicles that pass a specific location. Computers can also assess the number of shoppers who enter a store, the most popular entry point for train station commuters, or the peak time for cars to park in a parking garage.

When you want to get a more in-depth response from research participants, one method is to complete a one-on-one interview . One-on-one interviews allow the researcher to ask specific questions that match the respondent’s unique perspective as well as follow-up questions that piggyback on responses already completed. An interview allows the researcher to have a deeper understanding of the needs of the respondent, which is another strength of this type of data collection. The downside of personal interviews it that a discussion can be very time-consuming and results in only one respondent’s answers. Therefore, in order to get a large sample of respondents, the interview method may not be the most efficient method.

Taking the benefits of an interview and applying them to a small group of people is the design of a focus group . A focus group is a small number of people, usually 8 to 12, who meet the sample requirements. These individuals together are asked a series of questions where they are encouraged to build upon each other’s responses, either by agreeing or disagreeing with the other group members. Focus groups are similar to interviews in that they allow the researcher, through a moderator, to get more detailed information from a small group of potential customers (see Figure 6.5 ).

Link to Learning

Focus groups.

Focus groups are a common method for gathering insights into consumer thinking and habits. Companies will use this information to develop or shift their initiatives. The best way to understand a focus group is to watch a few examples or explanations. TED-Ed has this video that explains how focus groups work.

You might be asking when it is best to use a focus group or a survey. Learn the differences, the pros and cons of each, and the specific types of questions you ask in both situations in this article .

Preparing for a focus group is critical to success. It requires knowing the material and questions while also managing the group of people. Watch this video to learn more about how to prepare for a focus group and the types of things to be aware of.

One of the benefits of a focus group over individual interviews is that synergy can be generated when a participant builds on another’s ideas. Additionally, for the same amount of time, a researcher can hear from multiple respondents instead of just one. 11 Of course, as with every method of data collection, there are downsides to a focus group as well. Focus groups have the potential to be overwhelmed by one or two aggressive personalities, and the format can discourage more reserved individuals from speaking up. Finally, like interviews, the responses in a focus group are qualitative in nature and are difficult to distill into an easy statistic or two.

Combining a variety of questions on one instrument is called a survey or questionnaire . Collecting primary data is commonly done through surveys due to their versatility. A survey allows the researcher to ask the same set of questions of a large group of respondents. Response rates of surveys are calculated by dividing the number of surveys completed by the total number attempted. Surveys are flexible and can collect a variety of quantitative and qualitative data. Questions can include simplified yes or no questions, select all that apply, questions that are on a scale, or a variety of open-ended types of questions. There are four types of surveys (see Table 6.2 ) we will cover, each with strengths and weaknesses defined.

Let’s start off with mailed surveys —surveys that are sent to potential respondents through a mail service. Mailed surveys used to be more commonly used due to the ability to reach every household. In some instances, a mailed survey is still the best way to collect data. For example, every 10 years the United States conducts a census of its population (see Figure 6.6 ). The first step in that data collection is to send every household a survey through the US Postal Service (USPS). The benefit is that respondents can complete and return the survey at their convenience. The downside of mailed surveys are expense and timeliness of responses. A mailed survey requires postage, both when it is sent to the recipient and when it is returned. That, along with the cost of printing, paper, and both sending and return envelopes, adds up quickly. Additionally, physically mailing surveys takes time. One method of reducing cost is to send with bulk-rate postage, but that slows down the delivery of the survey. Also, because of the convenience to the respondent, completed surveys may be returned several weeks after being sent. Finally, some mailed survey data must be manually entered into the analysis software, which can cause delays or issues due to entry errors.

Phone surveys are completed during a phone conversation with the respondent. Although the traditional phone survey requires a data collector to talk with the participant, current technology allows for computer-assisted voice surveys or surveys to be completed by asking the respondent to push a specific button for each potential answer. Phone surveys are time intensive but allow the respondent to ask questions and the surveyor to request additional information or clarification on a question if warranted. Phone surveys require the respondent to complete the survey simultaneously with the collector, which is a limitation as there are restrictions for when phone calls are allowed. According to Telephone Consumer Protection Act , approved by Congress in 1991, no calls can be made prior to 8:00 a.m. or after 9:00 p.m. in the recipient’s time zone. 12 Many restrictions are outlined in this original legislation and have been added to since due to ever-changing technology.

In-person surveys are when the respondent and data collector are physically in the same location. In-person surveys allow the respondent to share specific information, ask questions of the surveyor, and follow up on previous answers. Surveys collected through this method can take place in a variety of ways: through door-to-door collection, in a public location, or at a person’s workplace. Although in-person surveys are time intensive and require more labor to collect data than some other methods, in some cases it’s the best way to collect the required data. In-person surveys conducted through a door-to-door method is the follow-up used for the census if respondents do not complete the mailed survey. One of the downsides of in-person surveys is the reluctance of potential respondents to stop their current activity and answer questions. Furthermore, people may not feel comfortable sharing private or personal information during a face-to-face conversation.

Electronic surveys are sent or collected through digital means and is an opportunity that can be added to any of the above methods as well as some new delivery options. Surveys can be sent through email, and respondents can either reply to the email or open a hyperlink to an online survey (see Figure 6.7 ). Additionally, a letter can be mailed that asks members of the survey sample to log in to a website rather than to return a mailed response. Many marketers now use links, QR codes, or electronic devices to easily connect to a survey. Digitally collected data has the benefit of being less time intensive and is often a more economical way to gather and input responses than more manual methods. A survey that could take months to collect through the mail can be completed within a week through digital means.

Design the Sample

Although you might want to include every possible person who matches your target market in your research, it’s often not a feasible option, nor is it of value. If you did decide to include everyone, you would be completing a census of the population. Getting everyone to participate would be time-consuming and highly expensive, so instead marketers use a sample , whereby a portion of the whole is included in the research. It’s similar to the samples you might receive at the grocery store or ice cream shop; it isn’t a full serving, but it does give you a good taste of what the whole would be like.

So how do you know who should be included in the sample? Researchers identify parameters for their studies, called sample frames . A sample frame for one study may be college students who live on campus; for another study, it may be retired people in Dallas, Texas, or small-business owners who have fewer than 10 employees. The individual entities within the sampling frame would be considered a sampling unit . A sampling unit is each individual respondent that would be considered as matching the sample frame established by the research. If a researcher wants businesses to participate in a study, then businesses would be the sampling unit in that case.

The number of sampling units included in the research is the sample size . Many calculations can be conducted to indicate what the correct size of the sample should be. Issues to consider are the size of the population, the confidence level that the data represents the entire population, the ease of accessing the units in the frame, and the budget allocated for the research.

There are two main categories of samples: probability and nonprobability (see Figure 6.8 ). Probability samples are those in which every member of the sample has an identified likelihood of being selected. Several probability sample methods can be utilized. One probability sampling technique is called a simple random sample , where not only does every person have an identified likelihood of being selected to be in the sample, but every person also has an equal chance of exclusion. An example of a simple random sample would be to put the names of all members of a group into a hat and simply draw out a specific number to be included. You could say a raffle would be a good example of a simple random sample.

Another probability sample type is a stratified random sample , where the population is divided into groups by category and then a random sample of each category is selected to participate. For instance, if you were conducting a study of college students from your school and wanted to make sure you had all grade levels included, you might take the names of all students and split them into different groups by grade level—freshman, sophomore, junior, and senior. Then, from those categories, you would draw names out of each of the pools, or strata.

A nonprobability sample is a situation in which each potential member of the sample has an unknown likelihood of being selected in the sample. Research findings that are from a nonprobability sample cannot be applied beyond the sample. Several examples of nonprobability sampling are available to researchers and include two that we will look at more closely: convenience sampling and judgment sampling.

The first nonprobability sampling technique is a convenience sample . Just like it sounds, a convenience sample is when the researcher finds a group through a nonscientific method by picking potential research participants in a convenient manner. An example might be to ask other students in a class you are taking to complete a survey that you are doing for a class assignment or passing out surveys at a basketball game or theater performance.

A judgment sample is a type of nonprobability sample that allows the researcher to determine if they believe the individual meets the criteria set for the sample frame to complete the research. For instance, you may be interested in researching mothers, so you sit outside a toy store and ask an individual who is carrying a baby to participate.

Collect the Data

Now that all the plans have been established, the instrument has been created, and the group of participants has been identified, it is time to start collecting data. As explained earlier in this chapter, data collection is the process of gathering information from a variety of sources that will satisfy the research objectives defined in step one. Data collection can be as simple as sending out an email with a survey link enclosed or as complex as an experiment with hundreds of consumers. The method of collection directly influences the length of this process. Conducting personal interviews or completing an experiment, as previously mentioned, can add weeks or months to the research process, whereas sending out an electronic survey may allow a researcher to collect the necessary data in a few days. 13

Analyze and Interpret the Data

Once the data has been collected, the process of analyzing it may begin. Data analysis is the distillation of the information into a more understandable and actionable format. The analysis itself can take many forms, from the use of basic statistics to a more comprehensive data visualization process. First, let’s discuss some basic statistics that can be used to represent data.

The first is the mean of quantitative data. A mean is often defined as the arithmetic average of values. The formula is:

A common use of the mean calculation is with exam scores. Say, for example, you have earned the following scores on your marketing exams: 72, 85, 68, and 77. To find the mean, you would add up the four scores for a total of 302. Then, in order to generate a mean, that number needs to be divided by the number of exam scores included, which is 4. The mean would be 302 divided by 4, for a mean test score of 75.5. Understanding the mean can help to determine, with one number, the weight of a particular value.

Another commonly used statistic is median. The median is often referred to as the middle number. To generate a median, all the numeric answers are placed in order, and the middle number is the median. Median is a common statistic when identifying the income level of a specific geographic region. 14 For instance, the median household income for Albuquerque, New Mexico, between 2015 and 2019 was $52,911. 15 In this case, there are just as many people with an income above the amount as there are below.

Mode is another statistic that is used to represent data of all types, as it can be used with quantitative or qualitative data and represents the most frequent answer. Eye color, hair color, and vehicle color can all be presented with a mode statistic. Additionally, some researchers expand on the concept of mode and present the frequency of all responses, not just identifying the most common response. Data such as this can easily be presented in a frequency graph, 16 such as the one in Figure 6.9 .

Additionally, researchers use other analyses to represent the data rather than to present the entirety of each response. For example, maybe the relationship between two values is important to understand. In this case, the researcher may share the data as a cross tabulation (see Figure 6.10 ). Below is the same data as above regarding social media use cross tabulated with gender—as you can see, the data is more descriptive when you can distinguish between the gender identifiers and how much time is spent per day on social media.

Not all data can be presented in a graphical format due to the nature of the information. Sometimes with qualitative methods of data collection, the responses cannot be distilled into a simple statistic or graph. In that case, the use of quotations, otherwise known as verbatims , can be used. These are direct statements presented by the respondents. Often you will see a verbatim statement when reading a movie or book review. The critic’s statements are used in part or in whole to represent their feelings about the newly released item.

Infographics

As they say, a picture is worth a thousand words. For this reason, research results are often shown in a graphical format in which data can be taken in quickly, called an infographic .

Check out this infographic on what components make for a good infographic. As you can see, a good infographic needs four components: data, design, a story, and the ability to share it with others. Without all four pieces, it is not as valuable a resource as it could be. The ultimate infographic is represented as the intersection of all four.

Infographics are particularly advantageous online. Refer to this infographic on why they are beneficial to use online .

Prepare the Research Report

The marketing research process concludes by sharing the generated data and makes recommendations for future actions. What starts as simple data must be interpreted into an analysis. All information gathered should be conveyed in order to make decisions for future marketing actions. One item that is often part of the final step is to discuss areas that may have been missed with the current project or any area of further study identified while completing it. Without the final step of the marketing research project, the first six steps are without value. It is only after the information is shared, through a formal presentation or report, that those recommendations can be implemented and improvements made. The first six steps are used to generate information, while the last is to initiate action. During this last step is also when an evaluation of the process is conducted. If this research were to be completed again, how would we do it differently? Did the right questions get answered with the survey questions posed to the respondents? Follow-up on some of these key questions can lead to additional research, a different study, or further analysis of data collected.

Methods of Quantifying Marketing Research

One of the ways of sharing information gained through marketing research is to quantify the research . Quantifying the research means to take a variety of data and compile into a quantity that is more easily understood. This is a simple process if you want to know how many people attended a basketball game, but if you want to quantify the number of students who made a positive comment on a questionnaire, it can be a little more complicated. Researchers have a variety of methods to collect and then share these different scores. Below are some of the most common types used in business.

Is a customer aware of a product, brand, or company? What is meant by awareness? Awareness in the context of marketing research is when a consumer is familiar with the product, brand, or company. It does not assume that the consumer has tried the product or has purchased it. Consumers are just aware. That is a measure that many businesses find valuable. There are several ways to measure awareness. For instance, the first type of awareness is unaided awareness . This type of awareness is when no prompts for a product, brand, or company are given. If you were collecting information on fast-food restaurants, you might ask a respondent to list all the fast-food restaurants that serve a chicken sandwich. Aided awareness would be providing a list of products, brands, or companies and the respondent selects from the list. For instance, if you give a respondent a list of fast-food restaurants and ask them to mark all the locations with a chicken sandwich, you are collecting data through an aided method. Collecting these answers helps a company determine how the business location compares to those of its competitors. 17

Customer Satisfaction (CSAT)

Have you ever been asked to complete a survey at the end of a purchase? Many businesses complete research on buying, returning, or other customer service processes. A customer satisfaction score , also known as CSAT, is a measure of how satisfied customers are with the product, brand, or service. A CSAT score is usually on a scale of 0 to 100 percent. 18 But what constitutes a “good” CSAT score? Although what is identified as good can vary by industry, normally anything in the range from 75 to 85 would be considered good. Of course, a number higher than 85 would be considered exceptional. 19

Customer Acquisition Cost (CAC) and Customer Effort Score (CES)

Other metrics often used are a customer acquisition cost (CAC) and customer effort score (CES). How much does it cost a company to gain customers? That’s the purpose of calculating the customer acquisition cost. To calculate the customer acquisition cost , a company would need to total all expenses that were accrued to gain new customers. This would include any advertising, public relations, social media postings, etc. When a total cost is determined, it is divided by the number of new customers gained through this campaign.

The final score to discuss is the customer effort score , also known as a CES. The CES is a “survey used to measure the ease of service experience with an organization.” 20 Companies that are easy to work with have a better CES than a company that is notorious for being difficult. An example would be to ask a consumer about the ease of making a purchase online by incorporating a one-question survey after a purchase is confirmed. If a number of responses come back negative or slightly negative, the company will realize that it needs to investigate and develop a more user-friendly process.

Knowledge Check

It’s time to check your knowledge on the concepts presented in this section. Refer to the Answer Key at the end of the book for feedback.

• Defining the problem
• Developing the research plan
• Selecting a data collection method
• Designing the sample
• you are able to send it to all households in an area
• it is inexpensive
• responses are automatically loaded into the software
• the data comes in quickly
• Primary data
• Secondary data
• Secondary and primary data
• Professional data
• It shows how respondents answered two variables in relation to each other and can help determine patterns by different groups of respondents.
• By presenting the data in the form of a picture, the information is easier for the reader to understand.
• It is an easy way to see how often one answer is selected by the respondents.
• This analysis can used to present interview or focus group data.

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Planning Your Data Collection: Designing methods for effective research

Planning your research is very important to obtain desirable results. In research, the relevance of data cannot be overlooked. It plays a pivotal role in laying a foundation to your study. Improper data has the potential to introduce bias and question the validity of your findings. Therefore, data collection is a critical step of any research project. It involves strategizing the process of gathering data to ensure its accuracy, and reliability. Without a thoughtful and systematic approach to data collection, researchers compromise the integrity and validity of their findings. By understanding the principles and strategies behind planning data collection, researchers and academicians can enhance the quality and impact of their research endeavors.

Table of Contents

What Is Data Collection?

Data collection is the systematic gathering and measurement of information from relevant sources to address a research problem. It forms the backbone of any research, as it helps in decision-making and builds the foundation to establish solid conclusions. A data collection plan is an outline of the steps to gather data for research.

Purpose of Data Collection

The data collection element of research is common to all fields of study. Collecting data without a proper strategy can result in inconclusive or unreliable findings. To ensure the success of your research, it is essential to develop a comprehensive data collection plan.

Types of Data Collection Methods

The data collection process is approached by various methods that can be categorized into quantitative, qualitative, and mixed approaches. However, it is important to understand each method and select the most appropriate one to effectively achieve your research objectives.

9 Steps for Planning Your Data Collection

Planning your data collection is important to achieve directionality in your research. Following a proper plan can help you to simplify your data collection procedure as it organizes the entire process. The steps for planning your data collection are as follows:

1. Define Your Research Objectives

Before beginning data collection, it is essential to clearly formulate your research objectives . Defining these objectives will facilitate the identification of the types of data that need to be collected. Formulate research questions and try to define them. This will establish a clear direction for the study.

2. Identify the Data Requirements

After defining the research objectives, you must identify the specific data elements required to address the research questions. Furthermore, identify the available and accessible data and assess its efficacy. Consider both qualitative and quantitative data sources, such as surveys, interviews, observations, existing datasets, or experiments.  Also, determine the level of detail for each data point and the suitable methods for data collection.

3. Select Appropriate Data Collection Methods

Choose data collection methods that align with your research objectives and data requirements. Evaluate the strengths and limitations of each methods and select the most appropriate approach accordingly. There are various data collection tools to choose from, such as interviews, role-playing, focus groups, in-person surveys, online surveys, telephonic surveys and observation. Assess the feasibility of the selected method and understand the pros and cons of each technique to make an informed decision.

4. Set a Realistic Timeline

Set a realistic timeline for your data collection process. This will not only help to organize your study but also ensures that you arrive at a conclusion within a specified timeframe. However, consider the time required for method design, pilot study, data interpretation, and analysis. Additionally, consider the available resources and constraints to create a feasible timeline.

5. Design the Method

After selecting your data collection process, design the method using the necessary instruments or resources. For surveys, create clear, concise, and unbiased questions that effectively capture the desired information. Develop interview protocols that cover the key topics you wish to explore. Carefully design observation protocols to ensure consistency and accuracy in data recording. Identify ways to collect maximum useful data and establish methods to interpret it. Also, determine ways to accurately measure the collected data.

6. Pilot Testing

Before launching your data collection, conduct a pilot test to evaluate the effectiveness of your instruments and procedures. A small-scale trial run allows you to identify any ambiguities in the data collection process. Also, make the necessary changes based on the pilot test feedback to enhance the reliability of your data.

7. Standardization

Establish a detailed standardized protocol based on the type of data and the results of the pilot testing. Also, record the specific instruments and standard conditions required for the study. Standardization of the protocol can facilitate the repetition of the study to check its reproducibility.

8. Establish Data Collection Procedures

Outline step-by-step procedures for data collection. Clearly document the process, including instructions for administering surveys , conducting interviews or observations, and handling any ethical considerations. Moreover, the researcher must be well-trained with the data collection method and must ensure its clear documentation.

9. Data Analysis Plan

Parallel to developing your data collection strategy, it is essential to plan your data analysis process. Determine and design the statistical methods or qualitative analysis techniques to derive meaningful insights from the collected data. Operationalize the data for variables that cannot be measured. Also, determine how to effectively represent your data.

Advantages and Disadvantages of Data Collection Methods

Understanding the advantages and disadvantages of the data collection method aids in planning your data collection method efficiently.

Following a structured plan for data collection can ease the process of research. Regardless of the field of the study, accurate and honest data collection plays a pivotal role in maintaining research integrity. Therefore, ethics should be at the forefront of any data collection strategy.

Ethical Reporting of the Collected Data ­— Especially with the dawn of AI

In the era of rapid technological advancements, the role of artificial intelligence (AI) in data collection and analysis has become increasingly prominent. Good research data management practices and principles of data sharing have become all the more crucial with the advent of AI in research data management. AI offers immense potential for processing and analyzing large datasets, enabling researchers to uncover valuable insights. However, using AI in data management accompanies several concerns.

While AI simplifies the hassles of data management, it must be handled following ethical principles. Therefore, it is crucial to address ethical considerations when reporting the collected data using AI to ensure responsible and transparent research practices. Are you a researcher clueless of managing your research data and understanding the proper use of AI? Watch this webinar “Important tips for managing your research data” for FREE and get some clarity.

Six ways to ensure ethical reporting of collected data are:

1. Informed Consent

Obtaining informed consent from participants is an integral part of ethical principles in research. Therefore, participants must be adequately informed about the procedures, potential risks, and the expected duration of their commitment.

2. Data Privacy

Respecting participants’ rights to privacy is an important ethical guideline that researchers must adhere to. When using AI for data collection, participants should be informed about the involvement of AI algorithms and how their data will be used. Also, researchers should employ adequate measures to protect participants’ data, such as anonymization and de-identification techniques. Prior to reporting data, personal identifiers should be removed or masked to prevent the identification of individuals. Furthermore, employing different privacy techniques can protect participants’ identities.

3. Transparency

Transparency becomes crucial when utilizing AI algorithms for data analysis. Therefore, researchers should strive to provide clear explanations of the algorithms used and how they influence the analysis and reporting of data. This includes disclosing any biases, limitations, or potential errors associated with the AI algorithms used. Consequently, transparent reporting enhances the accountability of research findings.

4. Mitigating Bias

AI algorithms are prone to inheriting biases present in the training data. Researchers must be aware of these biases and evaluate them. Additionally, AI systems should be monitored for potential biases related as race, gender, or other sensitive attributes. Therefore, actions should be taken to introduce fairness and mitigate bias in the reporting of results.

5. Ensuring Data Security

Safeguarding collected data against unauthorized access or breaches is vital to ensure data security. Implement robust security measures to protect data integrity and confidentiality. Also, utilize encryption techniques, access controls, and secure storage protocols to prevent unauthorized access or data leaks. Clearly communicate these security measures in your reporting to assure participants that their data is handled responsibly.

6. Ethical Reporting of Results

When reporting findings, researchers must present data accurately and objectively. Avoid misrepresentation or manipulation of data to support predetermined conclusions. Also, acknowledge uncertainties or limitations associated with the data collection process and the involvement of AI. Clearly document and provide detailed descriptions of the data collection methods, AI algorithms used, and data analysis techniques employed.

By prioritizing ethical considerations when reporting data collected using AI, researchers can uphold the integrity of their research and ensure the protection of participants’ rights. Transparently documenting the data collection process, ensuring data privacy, addressing bias in AI algorithms, and maintaining accountability and reproducibility contribute to responsible and trustworthy reporting practices in the AI-driven research landscape.

Have you faced any challenges during data collection for your research? Share your experience in the form of a thought piece or an article on Enago Academy Open Platform .

Frequently Asked Questions

Data collection is important because it helps in making informed decisions and provides the answers to your research questions.

Data collection in research is a step where information is gathered based on variables of interest, in a systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

Data collection methods can be broadly classified as: • Qualitative method: Qualitative methods deals with descriptive and conceptual data. • Quantitative method: Quantitative method deals with numerical data which can be ranked or measured. • Mixed method: Mixed method deals with both the numerical as well as the conceptual data.

Data can be collected for research using different means like interviews, surveys, observation, focus group discussion, forms, online monitoring, experiments, etc.

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Developing the Overall Research Plan

• Market Research
• Sustainable Businesses
• Supply Chain Management
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• Becoming an Owner
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The market research process consists of six discrete stages or steps:

• Step 1 - Articulate the research problem and objectives
• Step 2 - Develop the overall research plan
• Step 3 – Collect the data or information
• Step 4 – Analyze the data or information
• Step 5 – Present or disseminate the findings
• Step 6 – Use the findings to make the decision

This article focuses on Step 2. Developing a research plan is a complex undertaking because it involves so many different decisions.

Designing a market research plan begins with determining the most efficacious way to  collect the information . Innovative technology has enabled research tools to move to digital environments. Still, the fundamentals haven't changed: A market researcher must determine what data sources to use, the type of research approaches to take, how to  limit distortions of the data , what research instruments to employ, how a  sampling plan should be configured, how to protect participants' privacy, and what methods will be used to  contact research participants . Another critical concern is how much it will cost to implement the research plan.

Primary vs. Secondary Data

Before the market researcher can determine what research instruments to use, a decision must be made about the data sources. A market researcher can choose to collect primary data, secondary data, or both types of information. Primary data is gathered for the first time for a particular project or a specific purpose. Secondary data exists prior to the initialization of a new research project, having been collected for another purpose.

The fresh gathering of data, using primary research constitutes the main distinction between primary data and secondary data is that freshly gathered data is associated with primary research. A common form of primary research data is syndicated research, in which a group of researchers who are interested in the same research problem commission an independent market research provider to conduct a study and share the results with the purchasing participants.

While primary data seems the preferable course, a common and prudent practice is for a market researcher to explore possible secondary sources of data to determine if they will suffice to answer research questions.  Primary data collection can be expensive. Secondary data is generally of low cost or even available for free, and it is immediately available without having to wait for a research study to be completed.

Of course, a fundamental disadvantage of secondary data is that it will generally not have been configured precisely to fulfill a research agenda. As such, secondary data may be incomplete, inaccurate, dated, or unreliable. In such cases, a market researcher will necessarily need to commit to some type of primary data collection process.

Pilot Testing

Typically, primary data collection begins with some type of pilot testing, even if it is as simple as interviewing people in groups or individually to get the feel for  how people perceive some topic or question . Then a formal research instrument is developed, pilot-tested again for problems, and then used in the field to conduct the desired research, all according to the research plan. There are four main types of research data instruments available to market researchers:

Questionnaires or Surveys

For gathering primary research data , surveys are the most commonly used of the instruments. Although  the survey instrument is flexible and relatively inexpensive, it requires careful attention during development. All surveys should be pilot tested, at least to some degree, before they are released and administered to a target sample. The forms that the questions take should be carefully considered to ensure they perform as expected and that they fit well into the survey document as a whole. Developing survey questions is both an art and a science. Fortunately, many guidelines to survey construction, administration, and scoring are available.

Psychological Tools

Three commonly used psychological tools used to collect primary data are laddering questions techniques , in-depth interviews , and Rorschach-like tests.

• Laddering questions continue to probe deeper into the perspectives and opinions of respondents. The technique is iterative so that each subsequent question is generated according to the response to the previous question. Laddering is a technique that has been widely used in creative problem-solving methods and workshops. In-depth interviewing consists of probing ever deeper into the customer experience.
• The technique of in-depth interviewing was developed by Ernest Dichter. He differentiated between  qualitative research and quantitative research, referring to the former as head shrinking and the latter as nose counting . (Needless to say, Dichter was a proponent of qualitative research .)
• An interview technique similar to that used in Rorschach testing has been developed for market research by Gerald Zaltman of Olson Zaltman Associates. The instrument is known as the Zaltman Metaphoric Elicitation Technique (ZMET) and uses metaphoric images to access the associations that consumers have with certain product types. Typically, a participant in a ZMET-based study will collect images from a wide array of pictures that have no verbal content in order to  express the associated feelings and thoughts they have with regard to a product type.

Mechanical Devices

Mechanical devices are sometimes used to measure the physiological responses of research participants to product attributes or advertisements. Generally, what is measured is interest or emotions in response to what is seen, heard, felt, or smelled. Mechanical devices used in primary research data collection include Galvanometers, eye cameras, eye gaze recorders, audiometers, and tachistoscopes that show an image or ad for a brief flash.

Qualitative Measures

Qualitative measures are becoming more common in primary research as advanced in technology support different approaches, such as online surveys enabled by SurveyMonkey. Consumers are being turned loose with sophisticated technology on which they can record their impressions of product or aspects of their consumer experience.

Some market research provider agencies even go into the homes of consumers to film their interactions with products. These videos are trimmed down to a highlight reel that is used to analyze consumer behavior. One of the primary reasons for preferring qualitative measures to surveys or interviewing is that  the expressed beliefs and intentions of consumers often fail to match their actual behavior in the realm of brand engagement or purchase decisions.

Kotler, P. (2003). Marketing Management (11th ed.). Upper Saddle River, NJ: Pearson Education, Inc., Prentice Hall.

Lehmann, D. R. Gupta, S., and Seckel, J. (1997). Market Research. Reading, MA: Addison-Wesley.

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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Research Techniques pp 35–54 Cite as

Developing a Research Plan

• Habeeb Adewale Ajimotokan 2  
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Sadiku, M. N. O. (2000). Numerical techniques in electromagnetics . CRC Press.

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Ajimotokan, H.A. (2023). Developing a Research Plan. In: Research Techniques. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-13109-7_4

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the importance of a good research plan

Have you ever embarked on a research project and found yourself struggling to stay on track, or feeling lost and unsure of what to do next? A research plan can help you avoid these challenges and ensure that your research project is a success.

In this article, we'll dive into the key features of a research plan, and outline the steps you can take to create one for your research project. Whether you're a student, researcher, or professional, you'll learn what is the importance of having a research plan and how to make one that will help you achieve your research goals.

What is a Research Plan in a Project Management?

A research plan in project management can be thought of as a blueprint for the research that will be done as part of the project. Essentially, it's a roadmap that outlines everything from the background of the project to the methods and techniques that will be used, to the timeline and resources required to carry out the research.

At its core, the purpose of a research plan is to make sure the research is organized, and systematic and contributes to the overall success of the project.

What are the 5 purposes of research?

Research is at the heart of human progress, and it serves a variety of purposes. Here are five key reasons why research is essential:

Knowledge Expansion

Research helps us better understand the world around us, uncovering new information and deepening our understanding of existing knowledge.

Problem Solving

Through research, we can identify the root causes of complex issues and develop innovative solutions to tackle them.

Policy Development

Research findings inform evidence-based policymaking, ensuring that decisions are grounded in data and best practices.

Technological Advancements

Scientific research paves the way for groundbreaking inventions and technological advancements that shape our lives.

Skill Development

The research process hones critical thinking, problem-solving, and communication skills, which are essential in today's fast-paced, ever-changing world.

What are the methods of research?

Various research methods are available to choose from, depending on your research question and objectives. Here are a few common methods:

Qualitative Research

This method focuses on exploring human experiences and understanding the meanings people attach to their actions or surroundings. It often involves interviews, focus groups, and observations.

Quantitative Research

Quantitative research seeks to quantify data and analyze relationships between variables using statistical methods. Surveys, experiments, and numerical data analysis are common in this approach.

Mixed Methods

This approach combines both qualitative and quantitative methods, capitalizing on the strengths of each to provide a more comprehensive understanding of a research question.

Experimental Research

In this method, researchers manipulate one or more independent variables to observe their effect on a dependent variable, allowing for causal inferences.

Case Studies

Case studies involve an in-depth examination of a specific situation or example, offering rich insights into the complexities of real-world phenomena.

When selecting your research method, consider the goals and context of your study. Keep in mind that the choice of method can significantly impact the outcomes and conclusions drawn from your research.

What goes into a research plan?

Here are some of the key components you might expect to see in a research plan:

1.       Background: This section gives a brief overview of what the project is all about and why the research is being done.

2.      Objectives: Here, you'll find the clear and specific goals for the research, along with the questions that will be answered and the outcomes that are expected.

3.    Methods: This section lays out the different methods that will be used to gather information, such as surveys, interviews, focus groups, or experiments.

4.      Participants: You'll learn about the people who will be included in the research, along with the criteria for choosing them and how many participants there will be.

5.       Data collection: This section provides a detailed plan for how the data will be gathered, including the tools that will be used and the procedures for collecting and storing the information.

6.    Data analysis: Here, you'll find the plan for analyzing the data and what statistical methods will be used to do so.

7.       Timelines: This section outlines the schedule for carrying out the research, with deadlines for each step of the process.

8.      Budget: This part provides an estimate of the resources that will be required, including personnel, equipment, and materials.

9.       Ethical considerations: This section addresses important ethical issues, such as informed consent, confidentiality, and data protection.

Overall, a well-designed research plan is an essential part of successful project management, helping to minimize risk and reduce the chances of errors or delays.

Research Plan Features

Conducting a study can be compared to planning a road trip with your friends. Just like a well-planned road trip, a successful study requires a solid research plan. A research plan acts as a roadmap that guides you through the entire process, from start to finish, to ensure a successful outcome.

A study can have unexpected challenges and obstacles. For example, you may encounter bad weather or road closures on your trip. In a study, you may encounter unexpected challenges, like missing data or a lack of participants. But, with a well-planned research plan, you'll be prepared to handle these challenges and keep moving forward toward your destination.

Just like reaching your destination on a road trip, a successful study requires patience and persistence. You may encounter detours and delays, but with a clear roadmap, you'll be able to reach your destination. In a study, you may encounter setbacks, but with a solid research plan, you'll be able to overcome these challenges and achieve a successful outcome.

Here are some of the key features you need to include in your research plan:

Feature 1: Objectives and Goals - The Destination

Your research objectives and goals are like the destination you're trying to reach on your road trip. Just as you need to know where you're headed, your research plan should clearly define what you hope to achieve through your study. This includes defining the questions you want to answer, the outcomes you expect to see, and the impact you aim to have.

For example, if you're studying the effects of a new drug on patients with a specific illness, your objectives and goals might be to determine the drug's effectiveness and safety.

Feature 2: Methodology - The Route

Your methodology outlines the methods and techniques you'll use to conduct your study, just like choosing the best route for your road trip. This includes the study design, sample size, data collection methods, and analysis techniques. The methodology should be chosen based on your research question, available resources , and limitations of your study.

For example, if you're studying the impact of a new teaching method on student performance, your methodology might include conducting a randomized control trial to compare the new method to traditional teaching methods.

Feature 3: Timelines and Budgets - The Map

Your timelines and budgets act as the map you'll use to plan your road trip. Your research plan should include a schedule of when each aspect of your study will be completed and the resources you'll need to complete the project. These should be realistic and achievable, allowing for contingencies in case of unexpected events.

For example, if you're conducting a study on the effects of a new environmental policy on air quality, your timeline might include conducting air quality tests before and after the policy is implemented, and your budget might include the cost of the tests, equipment, and labor.

How to Write a Research Plan

Writing a research plan can seem overwhelming, especially if you're just starting. But trust me, having a solid plan in place will make the whole research process a lot smoother. A research plan is just a roadmap for your research project - it outlines your goals, the methods you'll use to achieve them, and the timeline for getting everything done.

So, where do you even begin with creating a research plan? Here's a step-by-step guide to help you get started:

Step 1: Find Your Focus - Define the Research Question

Before you dive into any research project, you need to have a clear idea of what you want to accomplish. The first step is to define the research question - this will serve as the cornerstone of your project. When formulating your research question, think about the problem you want to solve and how you want to approach it. It's important to make sure your research question is relevant, feasible, and aligns with the overall goals of your project.

Example: If you're interested in exploring the impact of social media on mental health, your research question could be "How does social media usage affect the mental well-being of young adults?"

Step 2: Get to Know the Literature - Review the Literature

Next, you'll want to familiarize yourself with what's already out there on your topic. This is where the literature review comes in - it will provide you with a comprehensive understanding of what's already known and what still needs to be explored. The literature review involves searching academic journals, books, and other sources for information on your topic. By the end of this step, you'll have a solid foundation of knowledge and a better idea of the gaps in the existing knowledge that your research project will fill.

Example: If your research question is about the impact of social media on mental health, you could search for articles and studies that have looked at the relationship between social media usage and mental well-being.

Step 3: Plan Your Attack - Develop the Methodology

Now that you have a good understanding of your topic and what's already out there, it's time to develop a plan for your research project. This is where you'll decide on the research design, sample size, data collection methods, and analysis techniques that will best address your research question. Your methodology should be based on the literature review and should be feasible, ethical, and reliable.

Example: If you're exploring the impact of social media on mental health, you could use a survey to gather data from young adults on their social media usage and mental well-being. You could also use statistical analysis to identify patterns and relationships between these variables.

Step 4: Get Organized - Prepare the Timeline and Budget

Finally, it's time to put all the pieces together and prepare a timeline and budget for your research project. This involves estimating the resources you'll need for each aspect of your project and creating a schedule for completing it. When developing your timeline and budget, it's important to be realistic, achievable, and flexible. Make sure to allow for unexpected events and contingencies.

Example: If you're exploring the impact of social media on mental health, your timeline could include steps like designing the survey, recruiting participants, collecting and analyzing data, and writing up the results. Your budget could include the cost of survey software, printing, and any other resources you'll need to complete the project.

How do you write a research plan on Edworking?

We understand the importance of a good research plan and how it can make or break your work. But where to begin? Enter Edworking, the all-in-one productivity platform that makes planning and executing research projects a breeze. In this article, we'll guide you on how to write a research plan on Edworking while providing helpful resources to empower you throughout the process.

Define your research objective

Before diving headfirst into the sea of research, it's essential to know your destination. What do you want to achieve with your research? By defining clear objectives, you'll be able to stay focused and streamline your efforts. Use Edworking's task management feature to create tasks and milestones for your objectives, keeping your research plan on track.

Identify your research questions

Once you've set your objectives, it's time to dig deeper. What are the burning questions that need answers? Listing these questions will help you stay on course and ensure you're gathering the right information. Try using the Stories feature in Edworking to share your questions with your team, encouraging open discussion and collaboration.

Outline your methodology

In the world of research, methodology is king. Decide which methods you'll use to collect and analyze data, and consider the ethical implications of your choices. Will you conduct interviews, surveys, or observe from afar? With Edworking's workspace, you can document your methodology in real-time, collaborate with your team, and even publish it as a blog.

Allocate resources and set a timeline

A good research plan needs a realistic timeline and proper resource allocation. Estimate how long each task will take, and assign resources accordingly. Edworking's task management tool lets you assign tasks to team members, track progress, and communicate updates seamlessly.

Monitor and adjust your research plan

Life is full of surprises, and your research plan is no exception. Keep an eye on your progress, and be ready to adapt to new information or unexpected obstacles. By using Edworking's integrated communication tools, you'll be able to pivot and make adjustments in real time, ensuring your research plan stays on course.

In conclusion, writing a research plan on Edworking is a walk in the park when you follow these steps. The platform's integrated features provide everything you need to create, manage, and execute your research plans, allowing you to focus on what truly matters: the success of your project. So, why wait? Sign up for a free demo on Edworking today and bring your research plans to life.

Thank you for taking the time to read this article on the importance of a good research plan. I hope you found it informative and helpful in your research journey. Remember, a solid research plan is the key to a successful research project and can make all the difference in achieving your goals and objectives.

If you're looking for a tool to help you create a research plan that's both well-structured and effective, I highly recommend checking out Edworking . This online platform provides you with all the tools you need to create a comprehensive research plan. With Edworking, you'll be able to streamline the research planning process and ensure that your project is a success. So why not give it a try today and see how it can help you reach your research destination with ease and confidence!

University Libraries

Library news, get expert help with your data management plan.

Do you need a data management and sharing plan (DMSP) for your grant proposal? The University of Iowa Libraries Research Data Services can help! Brian Westra, data services librarian, is available to help you create a data management plan in alignment with funding agency requirements. If you have questions about:

• Which repository is most appropriate for my data?
• What data standards apply to my data?
• What metadata should be used?
• Are there other agency-specific requirements to include in my plan?

We can help you create a data management plan to support your proposal. Research Data Services monitors funding agency policies and guidance, including the NIH policy.

Resources for NIH plans:

• NIH DMSP Checklist
• UI NIH Data Management and Sharing website
• NIH: Writing a Data Management and Sharing Plan

We can also assist with plans for the NSF, CDC, NOAA, and other funders.

Please contact Brian at [email protected] as early as possible to make sure you have plenty of time to submit your proposal.

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