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

Last updated

30 January 2024

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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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• A Research Guide
• Research Paper Guide

How to Write a Research Plan

• Research plan definition
• Purpose of a research plan
• Research plan structure
• Step-by-step writing guide

Tips for creating a research plan

• Research plan examples

Research plan: definition and significance

What is the purpose of a research plan.

• Bridging gaps in the existing knowledge related to their subject.
• Reinforcing established research about their subject.
• Introducing insights that contribute to subject understanding.

Research plan structure & template

Introduction.

• What is the existing knowledge about the subject?
• What gaps remain unanswered?
• How will your research enrich understanding, practice, and policy?

Literature review

Expected results.

• Express how your research can challenge established theories in your field.
• Highlight how your work lays the groundwork for future research endeavors.
• Emphasize how your work can potentially address real-world problems.

5 Steps to crafting an effective research plan

Step 1: define the project purpose, step 2: select the research method, step 3: manage the task and timeline, step 4: write a summary, step 5: plan the result presentation.

• Brainstorm Collaboratively: Initiate a collective brainstorming session with peers or experts. Outline the essential questions that warrant exploration and answers within your research.
• Prioritize and Feasibility: Evaluate the list of questions and prioritize those that are achievable and important. Focus on questions that can realistically be addressed.
• Define Key Terminology: Define technical terms pertinent to your research, fostering a shared understanding. Ensure that terms like “church” or “unreached people group” are well-defined to prevent ambiguity.
• Organize your approach: Once well-acquainted with your institution’s regulations, organize each aspect of your research by these guidelines. Allocate appropriate word counts for different sections and components of your research paper.

Research plan example

• Writing a Research Paper
• Research Paper Title
• Research Paper Sources
• Research Paper Problem Statement
• Research Paper Thesis Statement
• Hypothesis for a Research Paper
• Research Question
• Research Paper Outline
• Research Paper Summary
• Research Paper Prospectus
• Research Paper Proposal
• Research Paper Format
• Research Paper Styles
• AMA Style Research Paper
• MLA Style Research Paper
• Chicago Style Research Paper
• APA Style Research Paper
• Research Paper Structure
• Research Paper Cover Page
• Research Paper Abstract
• Research Paper Introduction
• Research Paper Body Paragraph
• Research Paper Literature Review
• Research Paper Background
• Research Paper Methods Section
• Research Paper Results Section
• Research Paper Discussion Section
• Research Paper Conclusion
• Research Paper Appendix
• Research Paper Bibliography
• APA Reference Page
• Annotated Bibliography
• Bibliography vs Works Cited vs References Page
• Research Paper Types
• What is Qualitative Research

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FLEET LIBRARY | Research Guides

Rhode island school of design, create a research plan: research plan.

• Research Plan
• Literature Review
• Ulrich's Global Serials Directory
• Related Guides

A research plan is a framework that shows how you intend to approach your topic. The plan can take many forms: a written outline, a narrative, a visual/concept map or timeline. It's a document that will change and develop as you conduct your research. Components of a research plan

1. Research conceptualization - introduces your research question

2. Research methodology - describes your approach to the research question

3. Literature review, critical evaluation and synthesis - systematic approach to locating,

    reviewing and evaluating the work (text, exhibitions, critiques, etc) relating to your topic

4. Communication - geared toward an intended audience, shows evidence of your inquiry

Research conceptualization refers to the ability to identify specific research questions, problems or opportunities that are worthy of inquiry. Research conceptualization also includes the skills and discipline that go beyond the initial moment of conception, and which enable the researcher to formulate and develop an idea into something researchable ( Newbury 373).

Research methodology refers to the knowledge and skills required to select and apply appropriate methods to carry through the research project ( Newbury 374) .

Method describes a single mode of proceeding; methodology describes the overall process.

Method - a way of doing anything especially according to a defined and regular plan; a mode of procedure in any activity

Methodology - the study of the direction and implications of empirical research, or the sustainability of techniques employed in it; a method or body of methods used in a particular field of study or activity *Browse a list of research methodology books  or this guide on Art & Design Research

Literature Review, critical evaluation & synthesis

A literature review is a systematic approach to locating, reviewing, and evaluating the published work and work in progress of scholars, researchers, and practitioners on a given topic.

Critical evaluation and synthesis is the ability to handle (or process) existing sources. It includes knowledge of the sources of literature and contextual research field within which the person is working ( Newbury 373).

Literature reviews are done for many reasons and situations. Here's a short list:

Sources to consult while conducting a literature review:

Online catalogs of local, regional, national, and special libraries

meta-catalogs such as worldcat , Art Discovery Group , europeana , world digital library or RIBA

subject-specific online article databases (such as the Avery Index, JSTOR, Project Muse)

digital institutional repositories such as Digital Commons @RISD ; see Registry of Open Access Repositories

Open Access Resources recommended by RISD Research LIbrarians

works cited in scholarly books and articles

print bibliographies

the internet-locate major nonprofit, research institutes, museum, university, and government websites

search google scholar to locate grey literature & referenced citations

trade and scholarly publishers

fellow scholars and peers

Communication                              

Communication refers to the ability to

• structure a coherent line of inquiry
• communicate your findings to your intended audience
• make skilled use of visual material to express ideas for presentations, writing, and the creation of exhibitions ( Newbury 374)

Research plan framework: Newbury, Darren. "Research Training in the Creative Arts and Design." The Routledge Companion to Research in the Arts . Ed. Michael Biggs and Henrik Karlsson. New York: Routledge, 2010. 368-87. Print.

About the author

Except where otherwise noted, this guide is subject to a Creative Commons Attribution license

source document

  Routledge Companion to Research in the Arts

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• Last Updated: Sep 20, 2023 5:05 PM
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How to Write a Research Plan

Your answers to these questions form your research strategy. Most likely, you’ve addressed some of these issues in your proposal. But you are further along now, and you can flesh out your answers. With your instructor’s help, you should make some basic decisions about what information to collect and what methods to use in analyzing it. You will probably develop this research strategy gradually and, if you are like the rest of us, you will make some changes, large and small, along the way. Still, it is useful to devise a general plan early, even though you will modify it as you progress. Develop a tentative research plan early in the project. Write it down and share it with your instructor. The more concrete and detailed the plan, the better the feedback you’ll get.

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This research plan does not need to be elaborate or time-consuming. Like your working bibliography, it is provisional, a work in progress. Still, it is helpful to write it down since it will clarify a number of issues for you and your professor.

Writing a Research Plan

To write out your research plan, begin by restating your main thesis question and any secondary ones. They may have changed a bit since your original proposal. If these questions bear on a particular theory or analytic perspective, state that briefly. In the social sciences, for example, two or three prominent theories might offer different predictions about your subject. If so, then you might want to explore these differences in your thesis and explain why some theories work better (or worse) in this particular case. Likewise, in the humanities, you might consider how different theories offer different insights and contrasting perspectives on the particular novel or film you are studying. If you intend to explore these differences, state your goal clearly in the research plan so you can discuss it later with your professor. Next, turn to the heart of this exercise, your proposed research strategy. Try to explain your basic approach, the materials you will use, and your method of analysis. You may not know all of these elements yet, but do the best you can. Briefly say how and why you think they will help answer your main questions.

Be concrete. What data will you collect? Which poems will you read? Which paintings will you compare? Which historical cases will you examine? If you plan to use case studies, say whether you have already selected them or settled on the criteria for choosing them. Have you decided which documents and secondary sources are most important? Do you have easy access to the data, documents, or other materials you need? Are they reliable sources—the best information you can get on the subject? Give the answers if you have them, or say plainly that you don’t know so your instructor can help. You should also discuss whether your research requires any special skills and, of course, whether you have them. You can—and should—tailor your work to fit your skills.

If you expect to challenge other approaches—an important element of some theses—which ones will you take on, and why? This last point can be put another way: Your project will be informed by some theoretical traditions and research perspectives and not others. Your research will be stronger if you clarify your own perspective and show how it usefully informs your work. Later, you may also enter the jousts and explain why your approach is superior to the alternatives, in this particular study and perhaps more generally. Your research plan should state these issues clearly so you can discuss them candidly and think them through.

If you plan to conduct tests, experiments, or surveys, discuss them, too. They are common research tools in many fields, from psychology and education to public health. Now is the time to spell out the details—the ones you have nailed down tight and the ones that are still rattling around, unresolved. It’s important to bring up the right questions here, even if you don’t have all the answers yet. Raising these questions directly is the best way to get the answers. What kinds of tests or experiments do you plan, and how will you measure the results? How will you recruit your test subjects, and how many will be included in your sample? What test instruments or observational techniques will you use? How reliable and valid are they? Your instructor can be a great source of feedback here.

Your research plan should say:

• What materials you will use
• What methods you will use to investigate them
• Whether your work follow a particular approach or theory

There are also ethical issues to consider. They crop up in any research involving humans or animals. You need to think carefully about them, underscore potential problems, and discuss them with your professor. You also need to clear this research in advance with the appropriate authorities at your school, such as the committee that reviews proposals for research on human subjects.

Not all these issues and questions will bear on your particular project. But some do, and you should wrestle with them as you begin research. Even if your answers are tentative, you will still gain from writing them down and sharing them with your instructor. That’s how you will get the most comprehensive advice, the most pointed recommendations. If some of these issues puzzle you, or if you have already encountered some obstacles, share them, too, so you can either resolve the problems or find ways to work around them.

Remember, your research plan is simply a working product, designed to guide your ongoing inquiry. It’s not a final paper for a grade; it’s a step toward your final paper. Your goal in sketching it out now is to understand these issues better and get feedback from faculty early in the project. It may be a pain to write it out, but it’s a minor sting compared to major surgery later.

Checklist for Conducting Research

• Familiarize yourself with major questions and debates about your topic.
• Is appropriate to your topic;
• Addresses the main questions you propose in your thesis;
• Relies on materials to which you have access;
• Can be accomplished within the time available;
• Uses skills you have or can acquire.
• Divide your topic into smaller projects and do research on each in turn.
• Write informally as you do research; do not postpone this prewriting until all your research is complete.

Back to How To Write A Research Paper .

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• Knowledge Base
• Methodology
• Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

Published on 3 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias .
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis.

Table of contents

Population vs sample, probability sampling methods, non-probability sampling methods, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, and many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

You are doing research on working conditions at Company X. Your population is all 1,000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee.

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample , every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

In a non-probability sample , individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalisable results.

You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g., by responding to a public online survey).

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

You send out the survey to all students at your university and many students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.

3. Purposive sampling

Purposive sampling , also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.

You want to know more about the opinions and experiences of students with a disability at your university, so you purposely select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to ‘snowballs’ as you get in contact with more people.

You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people she knows in the area.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Chapter 5. Sampling

Introduction.

Most Americans will experience unemployment at some point in their lives. Sarah Damaske ( 2021 ) was interested in learning about how men and women experience unemployment differently. To answer this question, she interviewed unemployed people. After conducting a “pilot study” with twenty interviewees, she realized she was also interested in finding out how working-class and middle-class persons experienced unemployment differently. She found one hundred persons through local unemployment offices. She purposefully selected a roughly equal number of men and women and working-class and middle-class persons for the study. This would allow her to make the kinds of comparisons she was interested in. She further refined her selection of persons to interview:

I decided that I needed to be able to focus my attention on gender and class; therefore, I interviewed only people born between 1962 and 1987 (ages 28–52, the prime working and child-rearing years), those who worked full-time before their job loss, those who experienced an involuntary job loss during the past year, and those who did not lose a job for cause (e.g., were not fired because of their behavior at work). ( 244 )

The people she ultimately interviewed compose her sample. They represent (“sample”) the larger population of the involuntarily unemployed. This “theoretically informed stratified sampling design” allowed Damaske “to achieve relatively equal distribution of participation across gender and class,” but it came with some limitations. For one, the unemployment centers were located in primarily White areas of the country, so there were very few persons of color interviewed. Qualitative researchers must make these kinds of decisions all the time—who to include and who not to include. There is never an absolutely correct decision, as the choice is linked to the particular research question posed by the particular researcher, although some sampling choices are more compelling than others. In this case, Damaske made the choice to foreground both gender and class rather than compare all middle-class men and women or women of color from different class positions or just talk to White men. She leaves the door open for other researchers to sample differently. Because science is a collective enterprise, it is most likely someone will be inspired to conduct a similar study as Damaske’s but with an entirely different sample.

This chapter is all about sampling. After you have developed a research question and have a general idea of how you will collect data (observations or interviews), how do you go about actually finding people and sites to study? Although there is no “correct number” of people to interview, the sample should follow the research question and research design. You might remember studying sampling in a quantitative research course. Sampling is important here too, but it works a bit differently. Unlike quantitative research, qualitative research involves nonprobability sampling. This chapter explains why this is so and what qualities instead make a good sample for qualitative research.

Quick Terms Refresher

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.
• Sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
• Sample size is how many individuals (or units) are included in your sample.

The “Who” of Your Research Study

After you have turned your general research interest into an actual research question and identified an approach you want to take to answer that question, you will need to specify the people you will be interviewing or observing. In most qualitative research, the objects of your study will indeed be people. In some cases, however, your objects might be content left by people (e.g., diaries, yearbooks, photographs) or documents (official or unofficial) or even institutions (e.g., schools, medical centers) and locations (e.g., nation-states, cities). Chances are, whatever “people, places, or things” are the objects of your study, you will not really be able to talk to, observe, or follow every single individual/object of the entire population of interest. You will need to create a sample of the population . Sampling in qualitative research has different purposes and goals than sampling in quantitative research. Sampling in both allows you to say something of interest about a population without having to include the entire population in your sample.

We begin this chapter with the case of a population of interest composed of actual people. After we have a better understanding of populations and samples that involve real people, we’ll discuss sampling in other types of qualitative research, such as archival research, content analysis, and case studies. We’ll then move to a larger discussion about the difference between sampling in qualitative research generally versus quantitative research, then we’ll move on to the idea of “theoretical” generalizability, and finally, we’ll conclude with some practical tips on the correct “number” to include in one’s sample.

Sampling People

To help think through samples, let’s imagine we want to know more about “vaccine hesitancy.” We’ve all lived through 2020 and 2021, and we know that a sizable number of people in the United States (and elsewhere) were slow to accept vaccines, even when these were freely available. By some accounts, about one-third of Americans initially refused vaccination. Why is this so? Well, as I write this in the summer of 2021, we know that some people actively refused the vaccination, thinking it was harmful or part of a government plot. Others were simply lazy or dismissed the necessity. And still others were worried about harmful side effects. The general population of interest here (all adult Americans who were not vaccinated by August 2021) may be as many as eighty million people. We clearly cannot talk to all of them. So we will have to narrow the number to something manageable. How can we do this?

First, we have to think about our actual research question and the form of research we are conducting. I am going to begin with a quantitative research question. Quantitative research questions tend to be simpler to visualize, at least when we are first starting out doing social science research. So let us say we want to know what percentage of each kind of resistance is out there and how race or class or gender affects vaccine hesitancy. Again, we don’t have the ability to talk to everyone. But harnessing what we know about normal probability distributions (see quantitative methods for more on this), we can find this out through a sample that represents the general population. We can’t really address these particular questions if we only talk to White women who go to college with us. And if you are really trying to generalize the specific findings of your sample to the larger population, you will have to employ probability sampling , a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. Why randomly? If truly random, all the members have an equal opportunity to be a part of the sample, and thus we avoid the problem of having only our friends and neighbors (who may be very different from other people in the population) in the study. Mathematically, there is going to be a certain number that will be large enough to allow us to generalize our particular findings from our sample population to the population at large. It might surprise you how small that number can be. Election polls of no more than one thousand people are routinely used to predict actual election outcomes of millions of people. Below that number, however, you will not be able to make generalizations. Talking to five people at random is simply not enough people to predict a presidential election.

In order to answer quantitative research questions of causality, one must employ probability sampling. Quantitative researchers try to generalize their findings to a larger population. Samples are designed with that in mind. Qualitative researchers ask very different questions, though. Qualitative research questions are not about “how many” of a certain group do X (in this case, what percentage of the unvaccinated hesitate for concern about safety rather than reject vaccination on political grounds). Qualitative research employs nonprobability sampling . By definition, not everyone has an equal opportunity to be included in the sample. The researcher might select White women they go to college with to provide insight into racial and gender dynamics at play. Whatever is found by doing so will not be generalizable to everyone who has not been vaccinated, or even all White women who have not been vaccinated, or even all White women who have not been vaccinated who are in this particular college. That is not the point of qualitative research at all. This is a really important distinction, so I will repeat in bold: Qualitative researchers are not trying to statistically generalize specific findings to a larger population . They have not failed when their sample cannot be generalized, as that is not the point at all.

In the previous paragraph, I said it would be perfectly acceptable for a qualitative researcher to interview five White women with whom she goes to college about their vaccine hesitancy “to provide insight into racial and gender dynamics at play.” The key word here is “insight.” Rather than use a sample as a stand-in for the general population, as quantitative researchers do, the qualitative researcher uses the sample to gain insight into a process or phenomenon. The qualitative researcher is not going to be content with simply asking each of the women to state her reason for not being vaccinated and then draw conclusions that, because one in five of these women were concerned about their health, one in five of all people were also concerned about their health. That would be, frankly, a very poor study indeed. Rather, the qualitative researcher might sit down with each of the women and conduct a lengthy interview about what the vaccine means to her, why she is hesitant, how she manages her hesitancy (how she explains it to her friends), what she thinks about others who are unvaccinated, what she thinks of those who have been vaccinated, and what she knows or thinks she knows about COVID-19. The researcher might include specific interview questions about the college context, about their status as White women, about the political beliefs they hold about racism in the US, and about how their own political affiliations may or may not provide narrative scripts about “protective whiteness.” There are many interesting things to ask and learn about and many things to discover. Where a quantitative researcher begins with clear parameters to set their population and guide their sample selection process, the qualitative researcher is discovering new parameters, making it impossible to engage in probability sampling.

Looking at it this way, sampling for qualitative researchers needs to be more strategic. More theoretically informed. What persons can be interviewed or observed that would provide maximum insight into what is still unknown? In other words, qualitative researchers think through what cases they could learn the most from, and those are the cases selected to study: “What would be ‘bias’ in statistical sampling, and therefore a weakness, becomes intended focus in qualitative sampling, and therefore a strength. The logic and power of purposeful sampling like in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the inquiry, thus the term purposeful sampling” ( Patton 2002:230 ; emphases in the original).

Before selecting your sample, though, it is important to clearly identify the general population of interest. You need to know this before you can determine the sample. In our example case, it is “adult Americans who have not yet been vaccinated.” Depending on the specific qualitative research question, however, it might be “adult Americans who have been vaccinated for political reasons” or even “college students who have not been vaccinated.” What insights are you seeking? Do you want to know how politics is affecting vaccination? Or do you want to understand how people manage being an outlier in a particular setting (unvaccinated where vaccinations are heavily encouraged if not required)? More clearly stated, your population should align with your research question . Think back to the opening story about Damaske’s work studying the unemployed. She drew her sample narrowly to address the particular questions she was interested in pursuing. Knowing your questions or, at a minimum, why you are interested in the topic will allow you to draw the best sample possible to achieve insight.

Once you have your population in mind, how do you go about getting people to agree to be in your sample? In qualitative research, it is permissible to find people by convenience. Just ask for people who fit your sample criteria and see who shows up. Or reach out to friends and colleagues and see if they know anyone that fits. Don’t let the name convenience sampling mislead you; this is not exactly “easy,” and it is certainly a valid form of sampling in qualitative research. The more unknowns you have about what you will find, the more convenience sampling makes sense. If you don’t know how race or class or political affiliation might matter, and your population is unvaccinated college students, you can construct a sample of college students by placing an advertisement in the student paper or posting a flyer on a notice board. Whoever answers is your sample. That is what is meant by a convenience sample. A common variation of convenience sampling is snowball sampling . This is particularly useful if your target population is hard to find. Let’s say you posted a flyer about your study and only two college students responded. You could then ask those two students for referrals. They tell their friends, and those friends tell other friends, and, like a snowball, your sample gets bigger and bigger.

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

My early experience with qualitative research was rather unique. At that time, I needed to do a project that required me to interview first-generation college students, and my friends, with whom I had been sharing a dorm for two years, just perfectly fell into the sample category. Thus, I just asked them and easily “gained my access” to the research subject; I know them, we are friends, and I am part of them. I am an insider. I also thought, “Well, since I am part of the group, I can easily understand their language and norms, I can capture their honesty, read their nonverbal cues well, will get more information, as they will be more opened to me because they trust me.” All in all, easy access with rich information. But, gosh, I did not realize that my status as an insider came with a price! When structuring the interview questions, I began to realize that rather than focusing on the unique experiences of my friends, I mostly based the questions on my own experiences, assuming we have similar if not the same experiences. I began to struggle with my objectivity and even questioned my role; am I doing this as part of the group or as a researcher? I came to know later that my status as an insider or my “positionality” may impact my research. It not only shapes the process of data collection but might heavily influence my interpretation of the data. I came to realize that although my inside status came with a lot of benefits (especially for access), it could also bring some drawbacks.

—Dede Setiono, PhD student focusing on international development and environmental policy, Oregon State University

The more you know about what you might find, the more strategic you can be. If you wanted to compare how politically conservative and politically liberal college students explained their vaccine hesitancy, for example, you might construct a sample purposively, finding an equal number of both types of students so that you can make those comparisons in your analysis. This is what Damaske ( 2021 ) did. You could still use convenience or snowball sampling as a way of recruitment. Post a flyer at the conservative student club and then ask for referrals from the one student that agrees to be interviewed. As with convenience sampling, there are variations of purposive sampling as well as other names used (e.g., judgment, quota, stratified, criterion, theoretical). Try not to get bogged down in the nomenclature; instead, focus on identifying the general population that matches your research question and then using a sampling method that is most likely to provide insight, given the types of questions you have.

There are all kinds of ways of being strategic with sampling in qualitative research. Here are a few of my favorite techniques for maximizing insight:

• Consider using “extreme” or “deviant” cases. Maybe your college houses a prominent anti-vaxxer who has written about and demonstrated against the college’s policy on vaccines. You could learn a lot from that single case (depending on your research question, of course).
• Consider “intensity”: people and cases and circumstances where your questions are more likely to feature prominently (but not extremely or deviantly). For example, you could compare those who volunteer at local Republican and Democratic election headquarters during an election season in a study on why party matters. Those who volunteer are more likely to have something to say than those who are more apathetic.
• Maximize variation, as with the case of “politically liberal” versus “politically conservative,” or include an array of social locations (young vs. old; Northwest vs. Southeast region). This kind of heterogeneity sampling can capture and describe the central themes that cut across the variations: any common patterns that emerge, even in this wildly mismatched sample, are probably important to note!
• Rather than maximize the variation, you could select a small homogenous sample to describe some particular subgroup in depth. Focus groups are often the best form of data collection for homogeneity sampling.
• Think about which cases are “critical” or politically important—ones that “if it happens here, it would happen anywhere” or a case that is politically sensitive, as with the single “blue” (Democratic) county in a “red” (Republican) state. In both, you are choosing a site that would yield the most information and have the greatest impact on the development of knowledge.
• On the other hand, sometimes you want to select the “typical”—the typical college student, for example. You are trying to not generalize from the typical but illustrate aspects that may be typical of this case or group. When selecting for typicality, be clear with yourself about why the typical matches your research questions (and who might be excluded or marginalized in doing so).
• Finally, it is often a good idea to look for disconfirming cases : if you are at the stage where you have a hypothesis (of sorts), you might select those who do not fit your hypothesis—you will surely learn something important there. They may be “exceptions that prove the rule” or exceptions that force you to alter your findings in order to make sense of these additional cases.

In addition to all these sampling variations, there is the theoretical approach taken by grounded theorists in which the researcher samples comparative people (or events) on the basis of their potential to represent important theoretical constructs. The sample, one can say, is by definition representative of the phenomenon of interest. It accompanies the constant comparative method of analysis. In the words of the funders of Grounded Theory , “Theoretical sampling is sampling on the basis of the emerging concepts, with the aim being to explore the dimensional range or varied conditions along which the properties of the concepts vary” ( Strauss and Corbin 1998:73 ).

When Your Population is Not Composed of People

I think it is easiest for most people to think of populations and samples in terms of people, but sometimes our units of analysis are not actually people. They could be places or institutions. Even so, you might still want to talk to people or observe the actions of people to understand those places or institutions. Or not! In the case of content analyses (see chapter 17), you won’t even have people involved at all but rather documents or films or photographs or news clippings. Everything we have covered about sampling applies to other units of analysis too. Let’s work through some examples.

Case Studies

When constructing a case study, it is helpful to think of your cases as sample populations in the same way that we considered people above. If, for example, you are comparing campus climates for diversity, your overall population may be “four-year college campuses in the US,” and from there you might decide to study three college campuses as your sample. Which three? Will you use purposeful sampling (perhaps [1] selecting three colleges in Oregon that are different sizes or [2] selecting three colleges across the US located in different political cultures or [3] varying the three colleges by racial makeup of the student body)? Or will you select three colleges at random, out of convenience? There are justifiable reasons for all approaches.

As with people, there are different ways of maximizing insight in your sample selection. Think about the following rationales: typical, diverse, extreme, deviant, influential, crucial, or even embodying a particular “pathway” ( Gerring 2008 ). When choosing a case or particular research site, Rubin ( 2021 ) suggests you bear in mind, first, what you are leaving out by selecting this particular case/site; second, what you might be overemphasizing by studying this case/site and not another; and, finally, whether you truly need to worry about either of those things—“that is, what are the sources of bias and how bad are they for what you are trying to do?” ( 89 ).

Once you have selected your cases, you may still want to include interviews with specific people or observations at particular sites within those cases. Then you go through possible sampling approaches all over again to determine which people will be contacted.

Content: Documents, Narrative Accounts, And So On

Although not often discussed as sampling, your selection of documents and other units to use in various content/historical analyses is subject to similar considerations. When you are asking quantitative-type questions (percentages and proportionalities of a general population), you will want to follow probabilistic sampling. For example, I created a random sample of accounts posted on the website studentloanjustice.org to delineate the types of problems people were having with student debt ( Hurst 2007 ). Even though my data was qualitative (narratives of student debt), I was actually asking a quantitative-type research question, so it was important that my sample was representative of the larger population (debtors who posted on the website). On the other hand, when you are asking qualitative-type questions, the selection process should be very different. In that case, use nonprobabilistic techniques, either convenience (where you are really new to this data and do not have the ability to set comparative criteria or even know what a deviant case would be) or some variant of purposive sampling. Let’s say you were interested in the visual representation of women in media published in the 1950s. You could select a national magazine like Time for a “typical” representation (and for its convenience, as all issues are freely available on the web and easy to search). Or you could compare one magazine known for its feminist content versus one antifeminist. The point is, sample selection is important even when you are not interviewing or observing people.

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

We have already discussed some of the differences in the goals of quantitative and qualitative sampling above, but it is worth further discussion. The quantitative researcher seeks a sample that is representative of the population of interest so that they may properly generalize the results (e.g., if 80 percent of first-gen students in the sample were concerned with costs of college, then we can say there is a strong likelihood that 80 percent of first-gen students nationally are concerned with costs of college). The qualitative researcher does not seek to generalize in this way . They may want a representative sample because they are interested in typical responses or behaviors of the population of interest, but they may very well not want a representative sample at all. They might want an “extreme” or deviant case to highlight what could go wrong with a particular situation, or maybe they want to examine just one case as a way of understanding what elements might be of interest in further research. When thinking of your sample, you will have to know why you are selecting the units, and this relates back to your research question or sets of questions. It has nothing to do with having a representative sample to generalize results. You may be tempted—or it may be suggested to you by a quantitatively minded member of your committee—to create as large and representative a sample as you possibly can to earn credibility from quantitative researchers. Ignore this temptation or suggestion. The only thing you should be considering is what sample will best bring insight into the questions guiding your research. This has implications for the number of people (or units) in your study as well, which is the topic of the next section.

What is the Correct “Number” to Sample?

Because we are not trying to create a generalizable representative sample, the guidelines for the “number” of people to interview or news stories to code are also a bit more nebulous. There are some brilliant insightful studies out there with an n of 1 (meaning one person or one account used as the entire set of data). This is particularly so in the case of autoethnography, a variation of ethnographic research that uses the researcher’s own subject position and experiences as the basis of data collection and analysis. But it is true for all forms of qualitative research. There are no hard-and-fast rules here. The number to include is what is relevant and insightful to your particular study.

That said, humans do not thrive well under such ambiguity, and there are a few helpful suggestions that can be made. First, many qualitative researchers talk about “saturation” as the end point for data collection. You stop adding participants when you are no longer getting any new information (or so very little that the cost of adding another interview subject or spending another day in the field exceeds any likely benefits to the research). The term saturation was first used here by Glaser and Strauss ( 1967 ), the founders of Grounded Theory. Here is their explanation: “The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation . Saturation means that no additional data are being found whereby the sociologist can develop properties of the category. As he [or she] sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. [They go] out of [their] way to look for groups that stretch diversity of data as far as possible, just to make certain that saturation is based on the widest possible range of data on the category” ( 61 ).

It makes sense that the term was developed by grounded theorists, since this approach is rather more open-ended than other approaches used by qualitative researchers. With so much left open, having a guideline of “stop collecting data when you don’t find anything new” is reasonable. However, saturation can’t help much when first setting out your sample. How do you know how many people to contact to interview? What number will you put down in your institutional review board (IRB) protocol (see chapter 8)? You may guess how many people or units it will take to reach saturation, but there really is no way to know in advance. The best you can do is think about your population and your questions and look at what others have done with similar populations and questions.

Here are some suggestions to use as a starting point: For phenomenological studies, try to interview at least ten people for each major category or group of people . If you are comparing male-identified, female-identified, and gender-neutral college students in a study on gender regimes in social clubs, that means you might want to design a sample of thirty students, ten from each group. This is the minimum suggested number. Damaske’s ( 2021 ) sample of one hundred allows room for up to twenty-five participants in each of four “buckets” (e.g., working-class*female, working-class*male, middle-class*female, middle-class*male). If there is more than one comparative group (e.g., you are comparing students attending three different colleges, and you are comparing White and Black students in each), you can sometimes reduce the number for each group in your sample to five for, in this case, thirty total students. But that is really a bare minimum you will want to go. A lot of people will not trust you with only “five” cases in a bucket. Lareau ( 2021:24 ) advises a minimum of seven or nine for each bucket (or “cell,” in her words). The point is to think about what your analyses might look like and how comfortable you will be with a certain number of persons fitting each category.

Because qualitative research takes so much time and effort, it is rare for a beginning researcher to include more than thirty to fifty people or units in the study. You may not be able to conduct all the comparisons you might want simply because you cannot manage a larger sample. In that case, the limits of who you can reach or what you can include may influence you to rethink an original overcomplicated research design. Rather than include students from every racial group on a campus, for example, you might want to sample strategically, thinking about the most contrast (insightful), possibly excluding majority-race (White) students entirely, and simply using previous literature to fill in gaps in our understanding. For example, one of my former students was interested in discovering how race and class worked at a predominantly White institution (PWI). Due to time constraints, she simplified her study from an original sample frame of middle-class and working-class domestic Black and international African students (four buckets) to a sample frame of domestic Black and international African students (two buckets), allowing the complexities of class to come through individual accounts rather than from part of the sample frame. She wisely decided not to include White students in the sample, as her focus was on how minoritized students navigated the PWI. She was able to successfully complete her project and develop insights from the data with fewer than twenty interviewees. [1]

But what if you had unlimited time and resources? Would it always be better to interview more people or include more accounts, documents, and units of analysis? No! Your sample size should reflect your research question and the goals you have set yourself. Larger numbers can sometimes work against your goals. If, for example, you want to help bring out individual stories of success against the odds, adding more people to the analysis can end up drowning out those individual stories. Sometimes, the perfect size really is one (or three, or five). It really depends on what you are trying to discover and achieve in your study. Furthermore, studies of one hundred or more (people, documents, accounts, etc.) can sometimes be mistaken for quantitative research. Inevitably, the large sample size will push the researcher into simplifying the data numerically. And readers will begin to expect generalizability from such a large sample.

To summarize, “There are no rules for sample size in qualitative inquiry. Sample size depends on what you want to know, the purpose of the inquiry, what’s at stake, what will be useful, what will have credibility, and what can be done with available time and resources” ( Patton 2002:244 ).

How did you find/construct a sample?

Since qualitative researchers work with comparatively small sample sizes, getting your sample right is rather important. Yet it is also difficult to accomplish. For instance, a key question you need to ask yourself is whether you want a homogeneous or heterogeneous sample. In other words, do you want to include people in your study who are by and large the same, or do you want to have diversity in your sample?

For many years, I have studied the experiences of students who were the first in their families to attend university. There is a rather large number of sampling decisions I need to consider before starting the study. (1) Should I only talk to first-in-family students, or should I have a comparison group of students who are not first-in-family? (2) Do I need to strive for a gender distribution that matches undergraduate enrollment patterns? (3) Should I include participants that reflect diversity in gender identity and sexuality? (4) How about racial diversity? First-in-family status is strongly related to some ethnic or racial identity. (5) And how about areas of study?

As you can see, if I wanted to accommodate all these differences and get enough study participants in each category, I would quickly end up with a sample size of hundreds, which is not feasible in most qualitative research. In the end, for me, the most important decision was to maximize the voices of first-in-family students, which meant that I only included them in my sample. As for the other categories, I figured it was going to be hard enough to find first-in-family students, so I started recruiting with an open mind and an understanding that I may have to accept a lack of gender, sexuality, or racial diversity and then not be able to say anything about these issues. But I would definitely be able to speak about the experiences of being first-in-family.

—Wolfgang Lehmann, author of “Habitus Transformation and Hidden Injuries”

Examples of “Sample” Sections in Journal Articles

Think about some of the studies you have read in college, especially those with rich stories and accounts about people’s lives. Do you know how the people were selected to be the focus of those stories? If the account was published by an academic press (e.g., University of California Press or Princeton University Press) or in an academic journal, chances are that the author included a description of their sample selection. You can usually find these in a methodological appendix (book) or a section on “research methods” (article).

Here are two examples from recent books and one example from a recent article:

Example 1 . In It’s Not like I’m Poor: How Working Families Make Ends Meet in a Post-welfare World , the research team employed a mixed methods approach to understand how parents use the earned income tax credit, a refundable tax credit designed to provide relief for low- to moderate-income working people ( Halpern-Meekin et al. 2015 ). At the end of their book, their first appendix is “Introduction to Boston and the Research Project.” After describing the context of the study, they include the following description of their sample selection:

In June 2007, we drew 120 names at random from the roughly 332 surveys we gathered between February and April. Within each racial and ethnic group, we aimed for one-third married couples with children and two-thirds unmarried parents. We sent each of these families a letter informing them of the opportunity to participate in the in-depth portion of our study and then began calling the home and cell phone numbers they provided us on the surveys and knocking on the doors of the addresses they provided.…In the end, we interviewed 115 of the 120 families originally selected for the in-depth interview sample (the remaining five families declined to participate). ( 22 )

Was their sample selection based on convenience or purpose? Why do you think it was important for them to tell you that five families declined to be interviewed? There is actually a trick here, as the names were pulled randomly from a survey whose sample design was probabilistic. Why is this important to know? What can we say about the representativeness or the uniqueness of whatever findings are reported here?

Example 2 . In When Diversity Drops , Park ( 2013 ) examines the impact of decreasing campus diversity on the lives of college students. She does this through a case study of one student club, the InterVarsity Christian Fellowship (IVCF), at one university (“California University,” a pseudonym). Here is her description:

I supplemented participant observation with individual in-depth interviews with sixty IVCF associates, including thirty-four current students, eight former and current staff members, eleven alumni, and seven regional or national staff members. The racial/ethnic breakdown was twenty-five Asian Americans (41.6 percent), one Armenian (1.6 percent), twelve people who were black (20.0 percent), eight Latino/as (13.3 percent), three South Asian Americans (5.0 percent), and eleven people who were white (18.3 percent). Twenty-nine were men, and thirty-one were women. Looking back, I note that the higher number of Asian Americans reflected both the group’s racial/ethnic composition and my relative ease about approaching them for interviews. ( 156 )

How can you tell this is a convenience sample? What else do you note about the sample selection from this description?

Example 3. The last example is taken from an article published in the journal Research in Higher Education . Published articles tend to be more formal than books, at least when it comes to the presentation of qualitative research. In this article, Lawson ( 2021 ) is seeking to understand why female-identified college students drop out of majors that are dominated by male-identified students (e.g., engineering, computer science, music theory). Here is the entire relevant section of the article:

Method Participants Data were collected as part of a larger study designed to better understand the daily experiences of women in MDMs [male-dominated majors].…Participants included 120 students from a midsize, Midwestern University. This sample included 40 women and 40 men from MDMs—defined as any major where at least 2/3 of students are men at both the university and nationally—and 40 women from GNMs—defined as any may where 40–60% of students are women at both the university and nationally.… Procedure A multi-faceted approach was used to recruit participants; participants were sent targeted emails (obtained based on participants’ reported gender and major listings), campus-wide emails sent through the University’s Communication Center, flyers, and in-class presentations. Recruitment materials stated that the research focused on the daily experiences of college students, including classroom experiences, stressors, positive experiences, departmental contexts, and career aspirations. Interested participants were directed to email the study coordinator to verify eligibility (at least 18 years old, man/woman in MDM or woman in GNM, access to a smartphone). Sixteen interested individuals were not eligible for the study due to the gender/major combination. ( 482ff .)

What method of sample selection was used by Lawson? Why is it important to define “MDM” at the outset? How does this definition relate to sampling? Why were interested participants directed to the study coordinator to verify eligibility?

Final Words

I have found that students often find it difficult to be specific enough when defining and choosing their sample. It might help to think about your sample design and sample recruitment like a cookbook. You want all the details there so that someone else can pick up your study and conduct it as you intended. That person could be yourself, but this analogy might work better if you have someone else in mind. When I am writing down recipes, I often think of my sister and try to convey the details she would need to duplicate the dish. We share a grandmother whose recipes are full of handwritten notes in the margins, in spidery ink, that tell us what bowl to use when or where things could go wrong. Describe your sample clearly, convey the steps required accurately, and then add any other details that will help keep you on track and remind you why you have chosen to limit possible interviewees to those of a certain age or class or location. Imagine actually going out and getting your sample (making your dish). Do you have all the necessary details to get started?

Table 5.1. Sampling Type and Strategies

Further Readings

Fusch, Patricia I., and Lawrence R. Ness. 2015. “Are We There Yet? Data Saturation in Qualitative Research.” Qualitative Report 20(9):1408–1416.

Saunders, Benjamin, Julius Sim, Tom Kinstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. “Saturation in Qualitative Research: Exploring Its Conceptualization and Operationalization.”  Quality & Quantity  52(4):1893–1907.

• Rubin ( 2021 ) suggests a minimum of twenty interviews (but safer with thirty) for an interview-based study and a minimum of three to six months in the field for ethnographic studies. For a content-based study, she suggests between five hundred and one thousand documents, although some will be “very small” ( 243–244 ). ↵

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

The actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).  Sampling frames can differ from the larger population when specific exclusions are inherent, as in the case of pulling names randomly from voter registration rolls where not everyone is a registered voter.  This difference in frame and population can undercut the generalizability of quantitative results.

The specific group of individuals that you will collect data from.  Contrast population.

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A sampling strategy in which the sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the sample.  This is often done through a lottery or other chance mechanisms (e.g., a random selection of every twelfth name on an alphabetical list of voters).  Also known as random sampling .

The selection of research participants or other data sources based on availability or accessibility, in contrast to purposive sampling .

A sample generated non-randomly by asking participants to help recruit more participants the idea being that a person who fits your sampling criteria probably knows other people with similar criteria.

Broad codes that are assigned to the main issues emerging in the data; identifying themes is often part of initial coding . 

A form of case selection focusing on examples that do not fit the emerging patterns. This allows the researcher to evaluate rival explanations or to define the limitations of their research findings. While disconfirming cases are found (not sought out), researchers should expand their analysis or rethink their theories to include/explain them.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

The result of probability sampling, in which a sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the random sample.  This is often done through a lottery or other chance mechanisms (e.g., the random selection of every twelfth name on an alphabetical list of voters).  This is typically not required in qualitative research but rather essential for the generalizability of quantitative research.

A form of case selection or purposeful sampling in which cases that are unusual or special in some way are chosen to highlight processes or to illuminate gaps in our knowledge of a phenomenon.   See also extreme case .

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

The accuracy with which results or findings can be transferred to situations or people other than those originally studied.  Qualitative studies generally are unable to use (and are uninterested in) statistical generalizability where the sample population is said to be able to predict or stand in for a larger population of interest.  Instead, qualitative researchers often discuss “theoretical generalizability,” in which the findings of a particular study can shed light on processes and mechanisms that may be at play in other settings.  See also statistical generalization and theoretical generalization .

A term used by IRBs to denote all materials aimed at recruiting participants into a research study (including printed advertisements, scripts, audio or video tapes, or websites).  Copies of this material are required in research protocols submitted to IRB.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Writing the Research Plan for Your Academic Job Application

By Jason G. Gillmore, Ph.D., Associate Professor, Department of Chemistry, Hope College, Holland, MI

A research plan is more than a to-do list for this week in lab, or a manila folder full of ideas for maybe someday—at least if you are thinking of a tenure-track academic career in chemistry at virtually any bachelor’s or higher degree–granting institution in the country. A perusal of the academic job ads in C&EN every August–October will quickly reveal that most schools expect a cover letter (whether they say so or not), a CV, a teaching statement, and a research plan, along with reference letters and transcripts. So what is this document supposed to be, and why worry about it now when those job ads are still months away?

What Is a Research Plan?

A research plan is a thoughtful, compelling, well-written document that outlines your exciting, unique research ideas that you and your students will pursue over the next half decade or so to advance knowledge in your discipline and earn you grants, papers, speaking invitations, tenure, promotion, and a national reputation. It must be a document that people at the department you hope to join will (a) read, and (b) be suitably excited about to invite you for an interview.

That much I knew when I was asked to write this article. More specifics I only really knew for my own institution, Hope College (a research intensive undergraduate liberal arts college with no graduate program), and even there you might get a dozen nuanced opinions among my dozen colleagues. So I polled a broad cross-section of my network, spanning chemical subdisciplines at institutions ranging from small, teaching-centered liberal arts colleges to our nation’s elite research programs, such as Scripps and MIT. The responses certainly varied, but they did center on a few main themes, or illustrate a trend across institution types. In this article I’ll share those commonalities, while also encouraging you to be unafraid to contact a search committee chair with a few specific questions, especially for the institutions you are particularly excited about and feel might be the best fit for you.

How Many Projects Should You Have?

While more senior advisors and members of search committees may have gotten their jobs with a single research project, conventional wisdom these days is that you need two to three distinct but related projects. How closely related to one another they should be is a matter of debate, but almost everyone I asked felt that there should be some unifying technique, problem or theme to them. However, the projects should be sufficiently disparate that a failure of one key idea, strategy, or technique will not hamstring your other projects.

For this reason, many applicants wisely choose to identify:

• One project that is a safe bet—doable, fundable, publishable, good but not earthshaking science.
• A second project that is pie-in-the-sky with high risks and rewards.
• A third project that fits somewhere in the middle.

Having more than three projects is probably unrealistic. But even the safest project must be worth doing, and even the riskiest must appear to have a reasonable chance of working.

How Closely Connected Should Your Research Be with Your Past?

Your proposed research must do more than extend what you have already done. In most subdisciplines, you must be sufficiently removed from your postdoctoral or graduate work that you will not be lambasted for clinging to an advisor’s apron strings. After all, if it is such a good idea in their immediate area of interest, why aren’t they pursuing it?!?

But you also must be able to make the case for why your training makes this a good problem for you to study—how you bring a unique skill set as well as unique ideas to this research. The five years you will have to do, fund, and publish the research before crafting your tenure package will go by too fast for you to break into something entirely outside your realm of expertise.

Biochemistry is a partial exception to this advice—in this subdiscipline it is quite common to bring a project with you from a postdoc (or more rarely your Ph.D.) to start your independent career. However, you should still articulate your original contribution to, and unique angle on the work. It is also wise to be sure your advisor tells that same story in his or her letter and articulates support of your pursuing this research in your career as a genuinely independent scientist (and not merely someone who could be perceived as his or her latest "flunky" of a collaborator.)

Should You Discuss Potential Collaborators?

Regarding collaboration, tread lightly as a young scientist seeking or starting an independent career. Being someone with whom others can collaborate in the future is great. Relying on collaborators for the success of your projects is unwise. Be cautious about proposing to continue collaborations you already have (especially with past advisors) and about starting new ones where you might not be perceived as the lead PI. Also beware of presuming you can help advance the research of someone already in a department. Are they still there? Are they still doing that research? Do they actually want that help—or will they feel like you are criticizing or condescending to them, trying to scoop them, or seeking to ride their coattails? Some places will view collaboration very favorably, but the safest route is to cautiously float such ideas during interviews while presenting research plans that are exciting and achievable on your own.

How Do You Show Your Fit?

Some faculty advise tailoring every application packet document to every institution to which you apply, while others suggest tweaking only the cover letter. Certainly the cover letter is the document most suited to introducing yourself and making the case for how you are the perfect fit for the advertised position at that institution. So save your greatest degree of tailoring for your cover letter. It is nice if you can tweak a few sentences of other documents to highlight your fit to a specific school, so long as it is not contrived.

Now, if you are applying to widely different types of institutions, a few different sets of documents will certainly be necessary. The research plan that you target in the middle to get you a job at both Harvard University and Hope College will not get you an interview at either! There are different realities of resources, scope, scale, and timeline. Not that my colleagues and I at Hope cannot tackle research that is just as exciting as Harvard’s. However, we need to have enough of a niche or a unique angle both to endure the longer timeframe necessitated by smaller groups of undergraduate researchers and to ensure that we still stand out. Furthermore, we generally need to be able to do it with more limited resources. If you do not demonstrate that understanding, you will be dismissed out of hand. But at many large Ph.D. programs, any consideration of "niche" can be inferred as a lack of confidence or ambition.

Also, be aware that department Web pages (especially those several pages deep in the site, or maintained by individual faculty) can be woefully out-of-date. If something you are planning to say is contingent on something you read on their Web site, find a way to confirm it!

While the research plan is not the place to articulate start-up needs, you should consider instrumentation and other resources that will be necessary to get started, and where you will go for funding or resources down the road. This will come up in interviews, and hopefully you will eventually need these details to negotiate a start-up package.

Who Is Your Audience?

Your research plan should show the big picture clearly and excite a broad audience of chemists across your sub-discipline. At many educational institutions, everyone in the department will read the proposal critically, at least if you make the short list to interview. Even at departments that leave it all to a committee of the subdiscipline, subdisciplines can be broad and might even still have an outside member on the committee. And the committee needs to justify their actions to the department at large, as well as to deans, provosts, and others. So having at least the introduction and executive summaries of your projects comprehensible and compelling to those outside your discipline is highly advantageous.

Good science, written well, makes a good research plan. As you craft and refine your research plan, keep the following strategies, as well as your audience in mind:

• Begin the document with an abstract or executive summary that engages a broad audience and shows synergies among your projects. This should be one page or less, and you should probably write it last. This page is something you could manageably consider tailoring to each institution.
• Provide sufficient details and references to convince the experts you know your stuff and actually have a plan for what your group will be doing in the lab. Give details of first and key experiments, and backup plans or fallback positions for their riskiest aspects.
• Hook your readers with your own ideas fairly early in the document, then strike a balance between your own new ideas and the necessary well referenced background, precedents, and justification throughout. Propose a reasonable tentative timeline, if you can do so in no more than a paragraph or two, which shows how you envision spacing out the experiments within and among your projects. This may fit well into your executive summary
• Show how you will involve students (whether undergraduates, graduate students, an eventual postdoc or two, possibly even high schoolers if the school has that sort of outreach, depending on the institutions to which you are applying) and divide the projects among students.
• Highlight how your work will contribute to the education of these students. While this is especially important at schools with greater teaching missions, it can help set you apart even at research intensive institutions. After all, we all have to demonstrate “broader impacts” to our funding agencies!
• Include where you will pursue funding, as well as publication, if you can smoothly work it in. This is especially true if there is doubt about how you plan to target or "market" your research. Otherwise, it is appropriate to hold off until the interview to discuss this strategy.

So, How Long Should Your Research Plan Be?

Learn more on the Blog

Here is where the answers diverged the most and without a unifying trend across institutions. Bottom line, you need space to make your case, but even more, you need people to read what you write.

A single page abstract or executive summary of all your projects together provides you an opportunity to make the case for unifying themes yet distinct projects. It may also provide space to articulate a timeline. Indeed, many readers will only read this single page in each application, at least until winnowing down to a more manageable list of potential candidates. At the most elite institutions, there may be literally hundreds of applicants, scores of them entirely well-suited to the job.

While three to five pages per proposal was a common response (single spaced, in 11-point Arial or 12-point Times with one inch margins), including references (which should be accurate, appropriate, and current!), some of my busiest colleagues have said they will not read more than about three pages total. Only a few actually indicated they would read up to 12-15 pages for three projects. In my opinion, ten pages total for your research plans should be a fairly firm upper limit unless you are specifically told otherwise by a search committee, and then only if you have two to three distinct proposals.

Why Start Now?

Hopefully, this question has answered itself already! Your research plan needs to be a well thought out document that is an integrated part of applications tailored to each institution to which you apply. It must represent mature ideas that you have had time to refine through multiple revisions and a great deal of critical review from everyone you can get to read them. Moreover, you may need a few different sets of these, especially if you will be applying to a broad range of institutions. So add “write research plans” to this week’s to do list (and every week’s for the next few months) and start writing up the ideas in that manila folder into some genuine research plans. See which ones survive the process and rise to the top and you should be well prepared when the job ads begin to appear in C&EN in August!

Jason G. Gillmore , Ph.D., is an Associate Professor of Chemistry at Hope College in Holland, MI. A native of New Jersey, he earned his B.S. (’96) and M.S. (’98) degrees in chemistry from Virginia Tech, and his Ph.D. (’03) in organic chemistry from the University of Rochester. After a short postdoctoral traineeship at Vanderbilt University, he joined the faculty at Hope in 2004. He has received the Dreyfus Start-up Award, Research Corporation Cottrell College Science Award, and NSF CAREER Award, and is currently on sabbatical as a Visiting Research Professor at Arizona State University. Professor Gillmore is the organizer of the Biennial Midwest Postdoc to PUI Professor (P3) Workshop co-sponsored by ACS, and a frequent panelist at the annual ACS Postdoc to Faculty (P2F) Workshops.

Other tips to help engage (or at least not turn off) your readers include:

• Avoid two-column formats.
• Avoid too-small fonts that hinder readability, especially as many will view the documents online rather than in print!
• Use good figures that are readable and broadly understandable!
• Use color as necessary but not gratuitously.

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Sampling is the statistical process of selecting a subset—called a ‘sample’—of a population of interest for the purpose of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviours within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalised back to the population of interest. Improper and biased sampling is the primary reason for the often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every US Presidential election.

The sampling process

As Figure 8.1 shows, the sampling process comprises of several stages. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organisation, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meet certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyse the behaviour of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behaviour over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population—usually a list with contact information—from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organisations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalisable to the population. For instance, if your target population is organisational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalisable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganisation and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms, most of which are medium or small sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P LargeCap, MidCap, or SmallCap lists, but includes publicly traded firms (and not private firms) and is hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to examine the success rate of a new ‘quit smoking’ program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular ‘quit smoking’ program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalisability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: every unit in the population has a known non-zero probability of being sampled, and the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called ‘strata’), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1,000 firms, you can start by categorising the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favour of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of the sample within each subgroup does not reflect the proportions in the sampling frame—or the population of interest—and the smaller subgroup (large-sized firms) is oversampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the ‘large’ group, 60 from the ‘medium’ group and 120 from the ‘small’ group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach are weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into ‘clusters’—usually along geographic boundaries—randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every office in those counties. However, depending on between-cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalisable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorise a sampling frame of firms into ‘high profitable’ firms and ‘low profitable firms’ based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Matched-pairs sampling techniques are often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools. Within each school, you can select a simple random sample of grade levels, and within each grade level, you can select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-probability sampling

Non-probability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, non-probability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalised back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping centre and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centres. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping centre such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping centre close to a university will attract primarily university students with unique purchasing habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalisability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalisable inferences.

Quota sampling. In this technique, the population is segmented into mutually exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70 per cent Caucasians, 15 per cent Hispanic-Americans, and 13 per cent African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping centre and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

Non-proportional quota sampling is less restrictive in that you do not have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African-Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping centre in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population, but may be useful in that it allows capturing the opinions of small and under-represented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample a group of corporate accountants who are familiar with this Act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalisable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also work in network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a ‘statistic’ is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not ‘statistic’ because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population. The difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to the corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical—and equal to the population parameter—and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Business Jargons

A Business Encyclopedia

Sampling Plan

Definition : A sampling plan provides an outline based on which the researcher performs research. Also, it provides a sketch required for ensuring that the data gathered is a representation of the defined target population. It is widely used in research studies. A researcher designs a sampling plan to prove that the data collected is valid and reliable for the concerned population.

It explains which category the researcher chooses for the survey. Also, it states the right sample size. Additionally, it expresses how the researcher has to be selected out of the population.

Issues Addressed by Sampling Plan

A sampling plan is the base from which the research starts. It includes the following three major decisions:

Sampling Unit

The researcher decides what the sampling unit should be. It involves choosing the category of the population to be surveyed. It defines the specific target population.

Example: In the Banking industry, the researcher decides: what should the sampling unit include. It may cover current account holders, saving account holders, or both.

The researcher takes such decisions at the time of designing the sampling frame. They do so to give all the elements of the target population an equal chance of getting included in the sample.

Sampling unit

The researcher has to determine the sample size. This means how many objects in the sample the researcher will survey. Generally, “the larger the sample size, the more is the reliability”. Therefore, researchers try to cover as many samples as possible.

Sampling Procedure

Which method should the researcher use to perform sampling ? For that, he must ensure that all the objects of the population have a fair and equal change of selection. Generally, researchers use probability sampling for determining the objects for selection. This is because probability sampling represents the sample more accurately.

In this regard, we are going to learn the two sampling methods :

Probability Sampling

• Simple Random Sampling : In this, every item of the sample has an equal chance of getting selected.
• Stratified Sampling : Here, the researcher divides the population into mutually exclusive groups, viz., age group. After that, the researcher will choose the elements randomly from each group.
• Cluster Sampling : Another name for cluster sampling is area sampling. In this, the researcher divides the population into existing groups or clusters. After that he chooses a sample of clusters on a random basis from the population.

However, the researcher usually finds probability sampling costly and time-consuming. In such a case, he can make use of non-probability sampling. It is a sampling by means of choice.

Non-Probability Sampling

• Convenience Sampling : Here, the researcher selects the easiest and most accessible population member.
• Judgment Sampling : Here, the researcher selects those members of the population whom he thinks that will contribute accurate information.
• Quota sampling : Here, the researcher interviews the fixed number of members of each category.

Thus, a researcher can select any kind of sample as per his convenience, subject to it fulfilling the purpose for which research takes place.

Steps involving Sampling Plan

An ideal sampling plan covers the following steps:

Define the target population

First of all, the researcher needs to decide and identify the group or batch for the study. The target population must be alloted identity by using descriptors. These descriptors indicate the characteristics of the elements. This will depict the target population frame.

Choose the data collection method

The researcher must choose a method for collecting the necessary data from the target population elements. For this, he uses information problem definition, data requirements and set research objectives.

Find out the sampling frames required

Once the researcher decides whom or what should be evaluated. The next step is to bring together a list of eligible sampling units. This list must have enough information about each prospective sampling unit. This allows the researcher can communicate with them. An incomplete sampling frame decreases the possibility of drawing a representative sample.

Pick the suitable sampling method

The researcher needs to pick any of the two types of sampling methods. The methods are probability and non-probability sampling. Usually, probability sampling yields better results. Also, it provides valid information about the target population’s criteria.

Ascertain necessary sample sizes and contract rates

The researcher must consider how accurate the sample estimates must be. Also, he needs to take into account how much time and money are available to collect data. To decide the right size of the sample, the researcher has to make the following decisions:

• Variability of population characteristics that is undergoing investigation.
• The confidence level is desired in the estimates.
• Degree of precision needed to estimate the population characteristic.

Design an operating plan for choosing the sample units

The researcher will design the actual procedures to use. He must include all the prospective respondents who form part of the sample.

Execute the operational plan

Carrying out data collection activities. This may involve actually talking to the prospective respondents by way of a telephone interview.

A word from Business Jargons

A sampling plan states the procedure for determining when the group under study is to be accepted or rejected. Further, if the sample gets rejected, the researcher must integrate corrective measures. He should do so after the complete inspection. After that, replacement of defective items with good ones takes place. We call this process a rectifying inspection.

Related terms:

• Stratified Sampling
• Sampling Methods
• Systematic Sampling
• Sampling Error
• Sampling Distribution of Proportion

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Legal research and writing

• Problem analysis

Research plan

• Legal encyclopedias and digests
• Legal dictionaries
• Annotated statutes
• Articles and papers
• Law reform commissions
• Blogs and website secondary sources
• British Columbia
• Finding case law
• Judicial treatment
• Legal writing
• Research guides
• Citation help
• Indigenous legal research methodology
• Research management

This page will help you analyze the legal problem you are going to research and help you identify the points your research will need to address.

Below  is a sample research plan template:

• Research Plan Format Sample (Nayyer) 2018

Planning your legal research

Once you have a handle on what your legal problem is you can plan your research accordingly.

The depth and focus of your plan will likely vary depending on the issues and your familiarity with the subject area.

Start with secondary materials

Start with secondary sources – discussions of the law – to get a grounding on the developed law and an idea of relevant legislation and leading cases on your topic. You'll find detail on secondary sources , including help in finding them, in the next section of this guide.

Legal dictionaries, legal encyclopedias, textbooks, annotated statutes, law reform commission reports, websites and blogs are all examples of secondary materials. Include these steps in your plan:

• Record the titles and dates of the material you look at
• Note down any legislation and cases that look relevant
• Make note of any potential keywords of search terms you come across

Identify relevant primary materials

Legislation is often the first primary source to consider as many legal research problems centre on the interpretation of legislation. Statutes, regulations and by-laws are all examples of legislation. Your research plan should include these steps:

• Write down the names of any potentially relevant legislation you are already aware of
• Add other legislation to this list as you conduct your research
• Update your legislation for currency
• Research your legislation for judicial interpretation

The other key primary source is case law . Be sure to pay attention to court level and jurisdiction. Your research plan should include these steps:

• Consider any leading cases you already know about for this issue
• Add other important cases to this list as you research secondary sources
• Add any cases you uncover as you note up legislation for judicial treatment
• Update or research the history of your cases for currency
• Note up your key cases for judicial treatment
• << Previous: Problem analysis
• Next: Secondary sources >>
• Last Updated: May 7, 2024 3:28 PM
• URL: https://libguides.uvic.ca/lrw

• identify the parameters to be measured, the range of possible values, and the required resolution
• design a sampling scheme that details how and when samples will be taken
• select sample sizes
• design data storage formats
• assign roles and responsibilities

• CREd Library , Planning, Managing, and Publishing Research

Developing a Five-Year Research Plan

Cathy binger and lizbeth finestack, doi: 10.1044/cred-pvd-path006.

The following is a transcript of the presentation videos, edited for clarity.

What Is a Research Plan, and Why Do You Need One?

Presented by Cathy Binger

First we’re going to talk about what a research plan is, why it’s important to write one, and why five years—why not one year, why not ten years. So we’ll do some of those basic things, then Liza is going to get down and dirty into the nitty-gritty of “now what” how do I go about writing that research plan.

First of all, what is a research plan? I’m sure some of you have taken a stab at these already. In case you haven’t, this is a real personalized map that relates your projects to goals. It’s exactly what it sounds like, it’s a plan of how you’re going to go about doing your research. It doesn’t necessarily just include research.

It’s something that you need to put a little time and effort into in the beginning. And then, if you don’t revisit it, it’s really a useless document. It’s something that you need to come back to repeatedly, at least annually, and you need to make it visible. So it’s not a document that sits around and once a year you pull it out and look at it.

It can and should be designed, especially initially, with the help of a mentor or colleague. And it does serve multiple purposes, with different lengths and different amounts of detail.

I forgot to say, too, getting started, the slides for this talk were started using as a jumping off point Ray Kent’s talk from last year. So some of the slides we’ve borrowed from him, so many thanks to him for that.

But why do we want to do a research plan? Well, to me the big thing is the vision. Dr. Barlow talked this morning about your line of research and really knowing where you want to go, and this is where that shows up with all the nuts and bolts in place.

What do you want to accomplish? What do you want to contribute? Most of you are at the stage in your career where maybe you have started out with that you want to change the world scenario and realized that whatever you wanted your first research project to be, really, is your entire career. You need to get that down to the point where it is manageable projects that you can do—this is where you map out what those projects are and set reasonable timelines for that.

You want to really demonstrate your independent thinking and your own creativity, whatever that is that you then establish as a PhD student, postdoc, and beyond—this is where you come back to, okay, here’s how I’m going to go about achieving all of that.

This next point, learning to realistically gauge how long it takes to achieve each goal, this for most of us is a phenomenally challenging thing to do. Most of us really overestimate what we can do in a certain amount of time, and we learn the hard way that you can’t, and that’s another reason why you keep coming back to these plans repeatedly and learning over time what’s really manageable, what’s really doable, so we can still reach our goals and be very strategic about how we do that.

When you’re not strategic, you just don’t meet the goals. Your time gets sucked into so many different things. We need to be really practical and strategic.

Everything we do is going to take longer than we think.

I think this last one is something that maybe we don’t talk about enough. Really being honest with ourselves about the role of research in our lives. Not all of you are at very high-level research universities. Some of you have chosen to go elsewhere, where research maybe isn’t going to be playing the same role as it is for other people. The research plan for someone at an R One research intensive university is going to look quite different from someone who is at a primary teaching university. We need to be open and practical about that.

Getting sidetracked. I love this picture, I just found this picture the other day. This feels like my life. You can get pulled in so many different directions once you are a professor. You will get asked to do a thousand different things. There are lots of great opportunities that are out there. Especially initially, it’s tempting to say yes to all of them. But if you’re going to be productive, you have to be very strategic. I’m going to be a little bit sexist against my own sex here for a minute, but my observation has been that women tend to fall into this a little bit more than men do in wanting to say yes and be people pleasers for everything that comes down the pike.

It is a professional skill to learn how to say no. And to do that in such a way that you are not burning bridges as you go down the path. That is a critical skill if you are going to be a successful researcher. I can’t tell you how many countless people I’ve seen who are very bright, very dedicated, have the skills that it takes in terms of doing the work—but then they are not successful because they’ve gotten sidetracked and they try to be too much of a good citizen, give too much service to the department, too much “sure I’ll take on that extra class” or whatever else comes down the line.

I just spoke with a professor recently who had something like five hours a week of office hours scheduled every single week for one class. Margaret is shaking her head like “are you kidding?” That’s crazy stuff. But he wanted to really support his students. His students loved him, but he was not going to get tenure. That’s the story.

So we have to be very thoughtful and strategic, and what can help you with this, and ASHA very firmly recognizes which is why we’re here—is that your mentors in your life should be there to help you learn these skills and learn what to say yes to, and learn what to say no to. I’ve learned to say things like, “Let me check with my mentor before I agree to that.” And it gives you a way out of that. The line that I use a lot is, “Let me check with my department head” or, I just said this to somebody last week, “I just promised my department head two weeks ago that I would only do X number of external workshops this year, so I’m going to have to turn this one down.” Those are really important skills to develop.

And having that research plan in place that you can go back to and say, know what, it’s not on my plan I can’t do it. If I do it—I have to go back to my research plan and figure out what I’m going to kick off in order to review this extra paper, in order to take on this extra task. The plan also helps me to know exactly what to say no to. And to be very direct and have a very strong visual.

I actually have my research plan up on a giant whiteboard in my office, so I can always go back to that and see where I am, and I can say, “Okay, what am I going to kick off of here? Nothing. Okay, I have to say no to whatever comes up.” Just be strategic. This is where I see most beginning professors really end up taking that wrong fork in the road—taking that right instead of that left, and ending up not being the successful researcher that they wanted to be.

What evidence supports research planning? This was something Ray Kent had found. That a recent analysis had found that postdoc scholars who developed a written plan with their postdoc advisers were much more productive than those who didn’t. And your performance during a postdoc—and I know many of you have either finished your postdoc or decided not to—so more simply, just during those first six years, the decisions you make really do establish the foundation for the rest of your professional life. It’s very important to get started and get off on the right foot.

I love this quote, I just found it the other day: “Productivity is never an accident. It is always the result of a commitment to excellence, intelligent planning, and focused effort.”

What we see with productivity is that postdoc scholars who developed written productivity expectations with their advisers were more productive than those who didn’t. You see 23% more papers submitted, 30% more first-author papers, and more grant proposals as well.

So why five years? I’m going to start with number 5. It’s long enough to build a program of research, but short enough to deal with changing circumstances. That’s really the long and the short of the matter. As well as these other things as well that I won’t take the time to go through point by point.

What Should a Five-Year Plan Include?

Presented by Lizbeth Finestack

So, thinking about a five-year research plan, I like to think about it like your major “To Do List.” It’s what you’re going to accomplish in five years. Start thinking: What is going to be on my to do list?

You can also think about it like: Okay, I have research. I’ve got to do research. Maybe think about this as one big bucket, or maybe one humongous silo. I have some farm themes going on. Cathy was just on a farm, so I thought I’d tie that in.

So here’s your big silo. You can call that your research silo.

But more realistically, you need to think about it like separate buckets, separate silos, where research is just one of those. Just like Cathy indicated, there’s going to be lots of other things coming up that you’re going to have to manage. They are going to have to be on your to do list, you need to figure out how to fit everything in.

What all those other buckets or silos are, are really going to depend on your job. And maybe the size of the silos, and the size of the buckets are going to vary depending on where you are, what the expectations are at your institution.

That’s important to keep in mind, and Cathy said this too, it’s not going to be the same for everyone. The five-year plan has to be your plan, your to do list.

Here are some buckets or some silos that I have on my list and the way that I break it up, this is just one example, take it or leave it.

The first three are all very closely related, right? Thinking about grants, thinking about research, thinking about publications. I’m going to define grants as actual writing, getting the grant, getting the money.

Research is what you’re going to do once you get that money. Steps you need to take before you are getting the money. Any sorts of projects, the lab work, that’s why I have the lab picture there. Of course, publications are part of the product—what’s coming out of the research—but it also cycles in because you need publications to support that you are a researcher to apply for funding and show you have this line of research that you’ve established and you’ll be able to continue. So, those first three are really closely related. And that’s where I’ll go next. And then have teaching and service you see here at the bottom.

So thinking about research, in that broad sense. As you’re writing your five-year plan you’re going to want to think of, “What’s my long-term goal?” There’s lots of ways to think of long-term goals. You could think, before I die, this is what I want to accomplish. For me I kind of have that. My long-term goal is that I’m going to find the most effective and efficient interventions for kids with language impairment. Huge broad goal. But within that I can start narrowing it down.

Where am I within that? Within the next five years or maybe the next ten years, what is it I want to accomplish towards that goal. Then start thinking about: In order to accomplish that goal, what are the steps I need to take? Starting to break it down a little bit. Then it’s also going to be really important to think: where are you going to start? Where are you now? What do you need to have happen? And is it reasonable to accomplish this goal within five years? Is it going to take longer? Maybe you could do it in a couple years? Start thinking about the timeline that’s going to work for you.

Then thinking about your goals—and everyone’s program is going to be different, like I said, there’s going to be a lot of individual needs, preferences. So it might be the case that you have this one long-term goal that you’re aiming for. Long-term goal in the sense of, maybe, what you want to study in your R01, perhaps something like that. But in order to get to that point, you’re going to have several short-term goals that need to be accomplished.

Or maybe it’s the case that you have two long-term goals. And with each of those you’re going to have multiple short-term goals that you’re working on. Maybe the scope of each of these long-term goals is a little bit less than in that first scenario.

Start thinking about my research, what I want to do, and how it might fit into these different circumstances.

Also thinking about your goals, this is a slide from Ray Kent from last year, was thinking about the different types of projects you might want to pursue, and thinking about ones that are definitely well on your way. They are safe bets. You have some funding. They are going to lead directly into your longer-term plan.

Those are going to be your front burner—things you can easily focus on. That said, don’t put everything there.

You can also have things on the back burner. Things that really excite you, might have huge benefits, big pay. But you don’t want to spend all of your time there because they could be pretty risky.

Start thinking about where you’re putting your time. Are you putting it all on this high-risk thing that if it doesn’t pan out you’re going to be in big trouble? Or balancing that somewhat with your front burner. Making that steady progress that will lead directly to help fund an R01 or whatever the mechanism that you’re looking for.

Then, thinking about your goals—if you have multiple long-term goals, or thinking about your short-term goals, you could think about your process. Is it something where you need to do study 1 then study 2, then study 3—each of those building on each other, that’s leading to that long-term goal. In many cases, that is the case, where you have to get information from the first study which is going to lead directly to the second study and so forth.

Or is it the case that you can be working on these three short-term goals simultaneously? Spreading your resources at the same time. Maybe it will take longer for any one study, but across a longer period of time you’ll get the information that you need to reach that long-term goal.

Lots and lots of different ways to go about it. The important thing is to think about what your needs are and what makes the most sense for you.

Here’s my own little personal example. Starting over here, I have my dissertation study. My dissertation study was this early efficacy study looking at one treatment approach using novel forms that really can’t generalize to anything too useful, but it was important.

Then I did a follow up study, where I was taking that same paradigm, looking to see where kids with typical development perform on the task. So I have these two studies, and they served as my preliminary studies for an R03. So I just finished an R03 where I was looking at different treatment approaching for kids with primary language impairment. At the same time, while conducting my R03, I’m also looking at some different approaches that might help with language development. Also conducting surveys to see what current practices are.

I have these three projects going on simultaneously, that are going to lead to a bigger pilot study that are going to feed directly into my R01. All of this will serve as preliminary data to go into an R01.

Start thinking about your projects, what you have. Maybe starting with your dissertation project or work that you’re doing as a postdoc as seeing how that can feed into your long-term goal. And really utilizing it, building on it, to your benefit.

That’s all fine and dandy. You can draw these great pictures. But you still have to break it down some more. It’s not like, “Oh, I’m just going to do this project.” There are other steps involved, and lots of the time these steps are going to be just as time consuming.

Starting to think about: well, if you have the funding. Saying, “I want to do this study, but I have no money to do it.” What are the steps in order to get the money to do it? Do you have a pilot study? What do you need?

Start thinking about the resources? Do you need to develop stimuli, protocols, procedures? Start working on that. All of these can be very time consuming, and if you don’t jump on that immediately, it’s going to delay when you can start that project.

Thinking about IRB. Relationships for recruitment, if you’re working with special populations especially? Do you have necessary personnel, grad students, people to help you with the project? Do you need to train them? What’s the timeline of the study?

Start thinking about all these pieces, and how they are going to fit in that timeline.

This is one way that might help you start thinking about the resources that you need. This is online—Ray Kent had it in his talk, and when I was doing my searches I came across it too and I have the website at the end. Just different ways to think about the resources you might need.

Let’s talk about mapping it out. You have your long-term goal. You have your short-term goals. You’re breaking it down thinking about all those little steps that you need to accomplish. We gotta put it on a calendar. When is it going to happen?

This is an example—you might have your five years. Each month plugging in what are you going to accomplish by that time. Maybe it’s when are grant applications due? It’s going to be important to put those on there to go what do I need to do to make that deadline. Maybe it’s putting when you’re going to get publications out. Things like that.

Honestly, looking at this drives me a little bit crazy, it seems a bit overwhelming. But it’s important to get to these details.

This is an example from, I did Lessons for Success a few years ago and they had their format for doing your plan. I wrote out all my projects, started thinking about all the different aspects. So if something like this works for you, by all means you could use that type of procedure.

Here’s a grid that Ray Kent showed last year. We’re breaking it down by semester. Thinking about each of your semesters, what manuscripts you’re going to be working on, what data collection, your grant applications. Starting to get into some of those other buckets: course preparation, conference submissions.

We also need to include teaching and service.

You probably can’t see this very well. This is similar to that last slide Ray Kent had used last year.

I have my five year plan: what studies I want to accomplish, start thinking about breaking it down.

Then at the beginning of each semester, I fill in a grid like this. Where at the top, I have each of my buckets. I have my grant bucket, my writing bucket which is going to include publications. I also include doing article reviews in my writing bucket, because that’s my writing time. My teaching bucket, my research bucket. Then at the end, my service bucket.

At the beginning of the semester, I think about the big things I want to accomplish. I list those at the top. Then at the beginning of each month, I say, okay what are the things I’m going to accomplish this month, write those in. Then at the beginning of each week, I start looking at whether I’m dedicating any time to the things I said I was going to do that month. I start listing those out saying, this is the amount of time I’m going to spend on that. Of course, I have to take data on what I actually do, so I plug in how much time I’m spending on each of the tasks. Then I graph it, because that’s rewarding to see how much time you’re spending on things, and I get a little side-tracked sometimes.

Think about a system that will help you keep on track, to make sure you’re meeting the goals that you want to meet in terms of your research. But also getting the other things done that you need to get done in terms of teaching and service.

Discussion and Questions

Compiled from comments made during the Pathways 2014 and 2015 conferences. (Video unavailable.)

Building Flexibility into Your Five-Year Plan Comments by Ray Kent, University of Wisconsin-Madison

The five-year plan is not a contract. It’s a map or a compass. A general set of directions to help you plan ahead. It’s not even a contract with yourself, because it will inevitably be revised in some ways.

Sometimes cool things land in your lap. Very often it turns out that through serendipity or whatever else, you find opportunities that are very enticing. Some of those can be path to an entirely new line of research. Some of them can be a huge distraction and a waste of time. It’s a really cool part of science that new things come along. If we put on blinders and say, “I’m committed to my research plan,” and we don’t look to the left or the right, we’re really robbing ourselves of much of the richness of the scientific life. Science is full of surprises, and sometimes those surprises are going to appear as research projects. The problem is you don’t want to redirect all your time and resources to those until you’re really sure they are going to pay off. I personally believe, some of those high risk but really appealing projects are things you can nurse along. You can devote some time and build some collaborations – far enough to determine how realistic and viable they are. That’s important because those things can be the core of your next research program.

It’s very easy to get overcommitted. We all know people who always say “yes”—and we know those people, and they are often disappointing because they can’t get things done. It’s important to have new directions, but limit them. Don’t say, “I’m going to have 12 new directions this year.” Maybe one or two. Weigh them carefully. Talk about them with other people to get a judgment about how difficult it might be to implement them. It enriches science: not only our knowledge, but the way we acquire new knowledge. A psychologist, George Miller—this is the guy with the magic number 7 +- 2—when we interviewed him years ago at Boystown, he said, “My conviction is that everybody should be able to learn a new area of study within three months.” That’s what he thought for a scientist was a goal.

The idea is that you can learn new things. And that’s very important because when you think of it in terms of a 30-year career, how likely is it that the project that you’re undertaking at age 28 is the same project you’ll be working on at age 68? Not very likely. You’re going to be reinventing yourself as a scientist. And reinventing yourself is one of the most important things you can do, because otherwise you’re going to be dead wood. Some projects aren’t worth carrying beyond five or ten years. They have an expiration date.

Building Risk into Your Five-Year Plan Comments by Ray Kent, University of Wisconsin-Madison

Your doctoral study should generally be low-risk research. As you move into a postdoctoral fellowship, think about having two studies—one low-risk, one high-risk with a potential for high impact. At this time you can begin to play the risk factor a little bit differently.

When you are tenure-track you can have a mix of significance with low-risk and high-risk studies. And when you are tenured, then you can go for high risk, clinical trials, and collaborations. Because you have established your independence, so you do not need to worry about losing your visibility. You can be recognized as a legitimate member of the team.

As you plan your career, you should take risk into account. Just as you manage your money taking risk into account, we should manage our careers taking risk into account. I have met people who did not really think about that, and they embarked on some very risky procedures and wasted a lot of time and resources with very little to show for it. For example, don’t put everything into an untested technology basket. You want to be using state of the art technology, but you want to be sure it is going to give you what you need.

Other Formats and Uses of Your Research Plan Audience Comments

• If you do your job right with your job talk, there’s a lot of cross-pollination between your job talk and your research plan. Ideally your job talk tells your colleagues that this is the long-term plan that you have. And they shouldn’t be surprised when you submit a more detailed research plan. They should say, “okay this is very consistent with the job talk.” In my view, the job talk should be a crystal summary of the major aspects of that research program. Of course, much of the talk will be about a specific project or two—but it should always be embedded within the larger program. That helps the audience keep sight of the fact that you are looking at the program. You can say that this is one project that I’ve done, and I plan to do more of these, and this is how they are conceptually related. That’s a good example of why the research plan has multiple purposes – it can be a research statement, it can be the core of your job talk, it can be the nature of your elevator message, and it can be a version of your research plan for a K award application or R01 application or anything else of that nature.
• I think what’s useful is to actually draft your NIH biosketch. The new biosketch has a section called “contributions to science.” It’s really helpful to think about all your projects. It’s hard to start with a blank sheet of paper. But to have it in the format of a biosketch can be really helpful.

Avoiding Overcommitment Audience Comments

• One of the things that is amazing about planning is that if you put an estimate on the level of effort for each part of your plan, you’ll quickly find that you are living three or four lives. Some 300% of your time is spent. It’s helpful for those of us who might share my lack of ability to see constraints or limitations to reel it back and say, “I have a lot on my plate.” Which allows you to say no—which is not something we all do very well when it comes to those nice colleagues and those people you want to impress nationally and connect with. But it allows you to look at what’s planned and go, “I don’t know where I’d find the time to do that.” Which will hopefully help you stay on track.
• I keep a to do list, but I also keep a “to not do” list. One of the things I will keep on my plan is the maximum number of papers I will review in a year. If I hit that number in March, that’s it. I say no to every other paper that comes down the pike. That’s something to work out with your mentor as far as what’s realistic and what’s okay for you. Every time I get a request, I think, “That’s my reading and writing time, so what am I willing to give up. If it means I won’t be able to write on my own paper this week, am I willing to do this?”

Staying on Schedule with Reading, Writing, and Reviewing Audience Comments

• You have to do what works for you. Some people do wait for big blocks of time for writing—which are hard to come by. But the most important thing is to block off your time. Put it on your schedule, or it is the first thing that will get pushed aside.
• Another thing I’ve done with some of my colleagues is writing retreats. So maybe once a year, twice a year, we’ll get together. Usually we’ll go to a hotel or somewhere, and we’re just writing. It’s a great way to get a jumpstart on a project. Like, I need to sit down and start this manuscript, and you can keep going once you’ve got that momentum.
• My input would be that you really have to write all the time, every day. It’s a skill. I’ve found that if I take time off, my writing deteriorates. It’s something you need to keep up with.
• I would look at it like a savings account that you put money into on a daily, weekly, monthly basis. The flip side of writing is reading. I would read constantly, widely, and not just in the discipline. That will give you not only a breadth in terms of your understanding of your field and the world around you, but it will also give you an incentive to make your own contributions. I think we don’t talk enough about the comprehensive side to this, and being receptive to the reading. I have a book, or something, by my bedside every night. And I read that until I fall asleep every night. And it’s done me in good stead over the years.
• Reviewing articles can help advance your career, but it is something you need to weigh carefully as a draw on your time. You get a lot from it. You get to see what’s out there. You get to see what’s coming down the pipe before publication. To me that’s a huge benefit. You get to learn from other people’s writing, and that’s part of your reading you get to do. But it is time consuming. And it depends on the kinds of papers you get. Sometimes you’re lucky and sometimes you’re not.
• If someone else is reviewing your grants and your articles, at some point you owe it back. You should at least be in break-even mode. Now, pre-tenure or postdoc your mentor should be doing that or senior faculty in the department. But there are so many articles to review. I review so many articles, but I am also at the tail end of my career. The bottom line is, if you don’t put on your schedule that if you don’t put time on your schedule for reading, reviewing articles forces you to look at and think about the literature, so you can be accomplishing what you owe back to the field—and at the same time, staying one step ahead knowledge wise. It forces you to do what you should be doing all along, which is keeping up with the literature.

Further Reading: Web Resources

Golash-Boza, T. (2014). In Response to Popular Demand, More on the 5-Year Plan. The Professor Is In . Available at http://theprofessorisin.com/2014/05/09/in-response-to-popular-demand-more-on-the-5-year-plan

Kelsky, K. (2010). The Five-Year Plan for Tenure-Track Professors. Get a life, PhD . Available at http://getalifephd.blogspot.com/2010/07/five-year-plan-for-tenure-track.html

National Association of Geoscience Teachers (NAGT). (2012). Planning Worksheets . Planning your Research Program (Available from the Science Education Resource Center at Carelton College Website at http://serc.carleton.edu/).

Pfirman, S., Bell, R., Culligan, P., Balsam, P. & Laird, J. (2008) . Maximizing Productivity and Recognition , Part 3: Developing a Research Plan. Science Careers. Available at http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2008_10_10/caredit.a0800148

Cathy Binger University of New Mexico

Lizbeth Finestack University of Minnesota

Based on a presentation and slides originally developed by Ray Kent, University of Wisconsin-Madison.

Presented at Pathways (2015). Hosted by the American Speech-Language-Hearing Association Research Mentoring Network.

Pathways is sponsored by the National Institute on Deafness and Other Communication Disorders (NIDCD) of the National Institutes of Health (NIH) through a U24 grant awarded to ASHA.

Copyrighted Material. Reproduced by the American Speech-Language-Hearing Association in the Clinical Research Education Library with permission from the author or presenter.

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• to make sure shielding is not compromised (exposure rate not greater than 0.5 mrem/hr at 5 cm from any surface of machine)
• to let workers know who to contact in an emergency or if they have any questions
• Safety evaluations will be performed initially when machine is purchased and after machine has been moved.
• Each machine should be key controlled when not in use. Interlocks, if present, must remain operational unless approved by the RSO.
• Shielding must be sufficient to maintain exposure rates less than 0.5 mrem/hr at 5 cm.
• The Radiation Safety Office will keep inventory and survey information on file in their offices. The SEM user should keep logbook of any maintenance done on machine. RSO must be notified if any modifications are made to the interlocks or any other safety devices. The SEM user should also keep a copy of operating and emergency procedures at the accelerator panel.
• No survey meters or personnel dosimetry are required.

References:

• Encyclopedia.Com
• Iowa State SEM Homepage
• Lawrence Livermore Radiation Safety Regulation, App. B, Summary of Radiation Generating Devices, Radiation Safety Requirements
• Virginia Tech Radiation Safety Pages

Many thanks also to the many responses and suggestions from the members of the RADSAFE list server.

If you have any questions please contact Radiological Management.

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Procrastinator's guide to Indiana's election: How to vote and what races are on the ballot

It's officially election week, which means it's time to make a voting plan for the May 7 primary election if you haven't already. Polls are open from 6 a.m. - 6 p.m. local time Tuesday.

IndyStar pulled together a primer of what you need to know in order to vote if you live in Central Indiana.

Live Election Day updates: How are polls today? What are voters saying about the Indiana primary?

IndyStar Election Night Live: Join IndyStar journalists, local pundits May 7 for live analysis of the primary's biggest races

How do I know if I'm registered to vote?

Voters can double check they are registered by going to indianavoters.in.gov and entering in their information.

If you are not yet registered to vote, it's too late to do so for the May primary. However, you can still register to vote in the November general election by going to indianavoters.in.gov .

Where can I vote?

In Marion County, voters can can cast a ballot at any vote center on Election Day. A list of available locations, as well as a map of sites, can be found at vote.indy.gov/vote-centers . Boone, Hendricks, Morgan, Johnson, Shelby and Hancock counties all allow voters to go to any vote center in the county as well.

Hamilton County residents have to vote at their assigned locations. You can find your voting location at indianavoters.in.gov .

What else to know before you head to the polls

• Decide which party you want to vote for: In Indiana, voters don't register with a particular political party, which means once you get to the polls, you'll have to tell the poll workers whether you want to pull a Republican or Democratic ballot. What you pick will impact the number of contested races you get to vote on.
• Bring your ID : Indiana law requires voters to show a government-issued photo ID that displays your name, photo and an expiration date of the last general election or later. Student IDs from an Indiana state school, not a private university, will work as long as they meet the above criteria.

What races will be on my ballot?

This year the following elected positions are up for election:

• President of the United States
• U.S. Senate
• U.S. House of Representatives
• State representatives
• State Senate (half of the seats)
• Other local races

But, not everyone will have a choice for every elected position. Some races are uncontested or feature no candidates. You can see who all will be on your specific ballot at indianavoters.in.gov .

Who is running for governor?

U.S. Sen. Mike Braun, Lt. Gov. Suzanne Crouch, former Secretary of Commerce Brad Chambers, Fort Wayne entrepreneur Eric Doden, former Indiana Attorney General Curtis Hill and mom-of-five Jamie Reitenour are running for governor on the Republican ballot.

IndyStar profiled each of the Republican candidates:

• Read Braun's here .
• Read Chambers' here .
• Read Crouch's here .
• Read Doden's here .
• Read Hills' here .
• Read Reitneour's here .

Jennifer McCormick, the former state schools superintendent, is the only choice on the Democratic ballot for governor.

Will I have a choice for U.S. Senate or president?

That depends on whether you pull a Republican or Democratic ballot.

For president, President Joe Biden is the only choice for the Democratic nominee. Meanwhile, Republicans can technically choose between former President Donald Trump and former U.N. ambassador Nikki Haley . Haley, though, dropped out of the race after she had qualified for Indiana's ballot.

For U.S. Senate, U.S. Rep. Jim Banks is the only Republican candidate who will be on the ballot. Democrats will have a choice between Rep. Marc Carmichael and Valerie McCray.

What other races should I read up on?

The following primary congressional races are poised to be competitive, two of which are located in central Indiana.

• Republican 3rd Congressional District primary : With Republican U.S. Rep. Jim Banks running for one of Indiana’s U.S. Senate seats, eight Republican candidates are running for the northeast Indiana district. Nonprofit executive Tim Smith, former Allen County Circuit Court judge Wendy Davis, former 3rd District Rep. Marlin Stutzman and state Sen. Andy Zay had raised the most money by mid-April, including personal loans.
• Republican 5th Congressional District primary : Nine Republican candidates are running for the this district, which stretches from Hamilton County north to Grant County. U.S. Rep. Victoria Spartz and Noblesville State Rep. Chuck Goodrich are the frontrunners, according to internal polling.
• Republican 6th Congressional District primary: U.S. Rep. Greg Pence decided not to seek reelection in this district that includes the southern portion of Marion County, which has led to a contentious Republican primary between seven candidates . Former Republican mayoral nominee Jefferson Shreve, state Rep. Mike Speedy, state Sen. Jeff Raatz, former lawmakers John Jacob and Bill Frazier, businessman Jamison Carrier and Darin Childress are running.
• Republican 8th Congressional District primary : U.S. Rep. Larry Bucshon is also not seeking reelection. Seven Republicans are running for the southwestern Indiana district. State Sen. Mark Messmer, R-Jasper, and former U.S. Rep. John Hostettler are the frontrunners.

There are a number of competitive Statehouse races , too. Three Hamilton County Republican primary races feature no incumbent lawmaker this year, due to the departures of state Reps. Jerry Torr, Donna Schaibley and Chuck Goodrich.

You can read all of IndyStar's election coverage here.

Contact IndyStar government and politics editor Kaitlin Lange at [email protected] or follow her on  X  @Kaitlin_Lange .

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State of University Address Reveals Campus Vibrancy Plan

May 7, 2024    |   By Jen Badie

During his State of the University address May 1 at the M&T Bank Exchange, University of Maryland, Baltimore (UMB) President Bruce E. Jarrell, MD, FACS, led the audience on a “tour” around West Baltimore and laid out his vision for a vibrant University and city.

First stop: the Hippodrome Theatre that adjoins the “jewel” of a building the 525 attendees were sitting in and was in a state of disrepair 40 years ago, now restored with the help of UMB. Jarrell took the audience members down a few blocks to the remodeled CFG Bank Arena, where he recently attended a sold-out concert on a Tuesday night.

President Jarrell speaks with an AI avatar during the State of the University Address.

“It ended at 10:30 and let out about the same time as the Hippodrome. You couldn’t walk, the streets were crowded, people were excited,” he said. “Maybe something good is happening around here.” (See photo gallery below.)

Jarrell envisions this same vibrancy — a place with culture, activity, and engagement — for the Lexington Street area of UMB’s campus, which sits near the remodeled Lexington Market. He announced that UMB is engaging private developers to convert the Lexington Street area into housing, stores, and research space for UMB students, faculty, staff, and others. UMB has created a development plan with consultants.

“We’d like to see this develop into an area that resembles a ‘College Town USA’ kind of atmosphere, an exciting place to be,” he said. “And we think we will be successful.”

Nearby sits Metro West, a building on Saratoga Street that has been vacant for 10 years.

“We’re very pleased that the Maryland Department of Health just next year is going to be moving into the Metro West building. That’s 1,200 people working there,” Jarrell said. “You can imagine how all of the people working there will flood Lexington Market, will give vibrancy to our campus.”

Behind Lexington Market, he said, UMB is making safety a priority on Eutaw Street and working to help people in need there through the EMBRACE initiative.

“The purpose is to get resources to people on the street to give them hope for their future,” he said.

School of Social Work Building

UMB will be constructing a new School of Social Work building, expected to open in 2027, in this area of campus at West Lexington and North Greene streets. Emphasizing the University’s commitment to sustainability, the building will include solar panels, geothermal energy, and green space.

“The important part about this area of development is that not only the School of Social Work but all of our schools will be immediately adjoining the Maryland Department of Health. There will be opportunity there and we will be able to influence them in positive ways and will become much closer to them,” he said. “This will add to UMB’s vibrancy.”

Next stop: the UMB BioPark and the nearly completed 4MLK building that will house the largest cluster of bioscience companies in the Greater Baltimore region.

“This building changes the landscape in a very dramatic way,” Jarrell said of 4MLK, adding that life sciences programs and a new joint bioengineering program with the University of Maryland, College Park (UMCP) are expected to be housed there.

He said the BioPark has given the University an anchor in West Baltimore that allows UMB to help the community and highlighted three programs: the UMB Community Engagement Center; the Live Near Your Work Program, a homebuying assistance program for employees; and a new population health initiative with the University of Maryland Medical Center that extends north of the BioPark to a clinical site at Mondawmin Mall.

He acknowledged that some people may be doubtful that the Lexington and Saratoga street areas can be transformed, but he pointed out that this type of vision has already come to fruition at the BioPark. Then-UMB President David Ramsay, DM, DPhil, envisioned the BioPark 20 years ago and faced doubters, too.

“There were a lot of people who said that nobody or no business would move across MLK Boulevard into the BioPark,” Jarrell said. “And now just look around. It’s become an essential part of this University, a West Campus, if you will. And it has been the enabler, the anchor for other programs that have enriched the local community. One advance has led to another and another in the BioPark. That’s what the BioPark has done: advance the community along with the buildings and other technology that’s developed there.

“Now I know there will be doubters that we can do this. They’ll say nobody will invest in this location. I disagree with them. I’m betting that people will. I’m sure that they will see the potential of this and the connection to our University and the vibrancy as a result of that.”

Face to Face with AI

Before the “tour,” Jarrell opened his speech with an often-humorous conversation with “Aisha,” a Chatbot GPT, highlighting the work that UMB is doing but showing some of the limitations of artificial intelligence (AI). Aisha brought up UMB’s “dogtorate” degree ceremony last year in which the University honored service and therapy dogs and their handlers.

“That story garnered 1.8 billion impressions. That’s billion with a B. That means many people saw the story. You got $25 million in free publicity,” she said, adding to laughter, “UMB should continue to give degrees to dogs.”

Jarrell, of course, said the University is not motivated by “likes” or the desire to be “trending” but by its impact on people’s health and well-being. He recalled hearing a speech by science fiction author Isaac Asimov and a quote that stuck with him that is applicable today to AI: “Science gathers knowledge faster than society gathers wisdom.”

“We should not be afraid of technology, of new discoveries,” Jarrell said. “Instead, we should embrace them and make sure that we control them, not they control us. She made me think, ‘Are we evolving fast enough as a university? Are we keeping up with Aisha and her kind? What is our strategy? How does she fit into our mission to improve the human condition?’ ”

UMB has started using AI in several programs including the University of Maryland Institute for Health Computing, a collaboration among the School of Medicine, the University of Maryland Medical System, and UMCP that uses AI to analyze clinical data to improve human health care.

He pointed out that two UMB schools — the Francis King Carey School of Law and the School of Social Work — have deep expertise in protecting people’s rights and privacy and social scientists to help develop wisdom in using AI.  

“Who better to help us guide our use of AI to benefit society, especially underserved communities, especially Baltimore? That makes UMB a perfect location, a perfect University to ask important social and legal questions as it applies to AI, how to responsibly use it. We just have to be strategic about it.”

‘Beehive of Activity’

During the conversation with Aisha, she asked Jarrell about the recent Supreme Court decision on affirmative action. Jarrell responded that it’s been a focus of leadership meetings as recently as that day.

“But just as we at UMB have persisted in our mission, we will persist in our commitment to equity, diversity, and inclusion,” he said. “That work is more important now than it ever has been.”

Jarrell highlighted numerous achievements throughout the schools: the two xenotransplantations done by School of Medicine faculty; the School of Social Work’s B’more for Healthy Babies program; the School of Nursing’s program to boost social inclusion and combat isolation in West Baltimore; and the School of Pharmacy’s first-of-its-kind medical cannabis program. He also talked about students such as the School of Dentistry’s Brian Garner, who is working with underserved patients at the Universities at Shady Grove, and educators such as Larry Gibson, Morton & Sophia Macht Professor of Law, who is retiring this year after educating three generations of law students. The law school recently launched the Gibson-Banks Center for Race and the Law.

Jarrell began his speech by asking Aisha what makes UMB great. She recited statistics such as how many students and employees the University has and told him she would not do his job for him, bringing laughter from the audience. Jarrell interjected with his perspective.

“UMB is great because of the people,” he said. “Everywhere I look at this University, I see a beehive of activity. People are committed to excellence in clinical care, in client care, in scholarly activity, teaching, service. It’s a phenomenal place, and they’re here all hours of the day and night. It’s a way of life here. We’re very fortunate to be at a place like this.”

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