research plan design and technology

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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FLEET LIBRARY | Research Guides

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

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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.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research Design 101: A Guide To Planning Experiment Design

Brigitta Puskás

Every day, we conduct research. Every research study has its own purpose it lines up with. But how do our researchers plan their research ? What methods for designing research reflect the goals and delivers results? In this article, we go back to the very basics of research and its types. Then, we walk you through our process of assumption validation and experiment design in an everyday setting.

What we will cover in this article:

• The basic types of research
• The different types of research methods
• Study design in research
• The types of qualitative research and a research design in qualitative research
• The types of quantitative research and a research design in quantitative research

The research problem defines research design

According to American sociologist Earl Robert Babbie, “Research is a systematic inquiry to describe, explain, predict and control the observed phenomenon.”

The design of your research , on the other hand, provides your customized toolkit for a specific research problem. You need to make sure that the tools fit the problem. Research design represents the set of methods and procedures you utilize during the process of data collection and analysis specified in the research problem.

We create a research design as a framework to deliver answers to research questions. Based on the research problem, the design of a study defines defines:

• The right choice of study type (descriptive or experimental)
• Sub-type (e.g., descriptive-longitudinal case study)
• The hypotheses
• The independent and dependent variables
• The scope of experimental design
• Data collection methods and a statistical analysis plan, if applicable.

Types of research: Inductive and deductive research

You will find this familiar if you have ever written a thesis. Basically, you can start researching a subject from two ends.

We use inductive research methods to analyze a phenomenon, while deductive research methods verify it.

To put it into practice: We either want to analyze why more people spend more time texting on weekends than on weekdays (inductive research), or assume that it results from them having more time on those days — and then we test this assumption (deductive research).

We associate inductive research approaches generally with qualitative methods and techniques, while deductive methods connect more to quantitative research.

Researching business and technology

The above holds true for any type of research, from physics to neurology, ornithology to user research. “Average people” don’t usually deal with all these fancy research-related expressions (other than that one time with your thesis paper back in college).

But businesses and tech companies do research all the time as well. In a business setting, researchers mainly ask:

• What do organizations or businesses really want to find out?
• What processes and mechanisms need analyzing to chase the idea?
• What arguments need building up around a concept?
• What evidence will people require to believe in the idea or concept?

Research purposes

Research serves three purposes, depending on prior knowledge and the context. We might not even know what will come out in the end (exploratory research). We might want to structure already existing information in a newer / better way (descriptive research) or to find explanations for a given phenomenon.

Let’s dive more into detail!

1. Exploratory research

If we want to explore the phenomenon and research questions but don’t know for sure whether to offer a final conclusion, choose explanatory research. Conduct this type of research to take a look at new problem areas which no one has explored yet.

For example, we want to know what people use their phones for during the week and on weekends. We dive into what apps exist, how we can group them, how people choose, how their prioritize apps, etc.

Exploratory research proves essential for laying the foundation for more conclusive research and data collection.

2. Descriptive Research:

Descriptive research focuses on shedding light on specific issues through the process of data collection. Lead these studies to describe a behavior or phenomenon.

Descriptive research has three main goals: describing, explaining and validating research findings.

For example, we look at when people use apps and what for.

3. Explanatory Research:

Conduct explanatory research or causal research to understand the impact of certain changes in existing standard procedures. Conducting experiments represents the most popular form of casual research, such as research conducted to understand the effect of rebranding on customer loyalty.

For example, we look at why people seem to use their phones longer on average on weekends than on weekdays.

The research process

We broadly classify research methods as qualitative research and quantitative research.

Both methods have distinctive properties and data collection methods. In this segment, we will learn more about both.

Whichever research method you decide to go with, first evaluate the problem from an analytical point of view.

Qualitative research design: Types of qualitative research

As a research method, qualitative research collects data using conversational methods in which participants involved in the research answer open-ended questions. We collect the essentially non-numerical responses.

This method not only helps a researcher understand what participants think but also why they think in a particular way.

These qualitative research methods see wide usage:

• One-to-one Interviews
• Focus Groups
• Ethnographic Research
• Text Analysis
• Case Study Research

Quantitative research design: Types of quantitative research

Quantitative research methods deal with numbers and anything that can deal with a measurable form in a systematic way of investigating the phenomenon. We use it to answer questions in terms of justifying relationships with measurable variables to explain, predict or control a phenomenon.

Researchers often use three methods to conduct this type of research

• Survey Research
• Descriptive Research
• Correlational Research

What makes up research design? Identifying the ideal research methodologies

To choose the appropriate research methods, you must clearly identify the research objectives. Take into consideration this example of research objectives you may have for your business:

• First, find out your clients’ needs.
• Know their preferences and understand what they find important.
• Find an appropriate way to make them aware of your products and services.
• Find ways to improve your products or services to suit your customers’ needs.

After identifying what you need to know, ask which research methods will offer you that information.

Organize your questions within the framework of the 7 Ps of marketing, which influences your company – product, price, promotion, place, people, processes and physical tests.

Research methods in psychology

Psychologists use many different methods for conducting research. Each has advantages and disadvantages that make it suitable for certain situations and unsuitable for others.

Case studies, surveys, naturalistic observation and laboratory observation exemplify descriptive or correlational research methods. Using them, researchers can describe different events, experiences or behaviors, and look for links between them. However, they do not enable researchers to determine causes of behavior.

Remember: Correlation Is Not Causation! Two factors may have a connection without one causing the other to occur. Often, a third factor explains the correlation.

Why does it matter to know the basics of psychological research? Because in any situation when we deal with people, psychological occurrences might come into play.

Differences between research methods and research design

Research methods.

Generalized and established, research methods address research questions (e.g., qualitative vs. quantitative methods). Not all methods apply for all research questions, so the area of research that you want to explore limits the choice of method.

Research Design

Research design involves determining how to apply your chosen method to answer your research question. Think of your study’s design as a blueprint detailing what to do and how to accomplish it.

Key aspects of research design include research methodology, participant/sample collection and assignment and data collection procedures and instruments.

Relationship

Think of the choice of research methods, then design a reciprocal process extending well into your study. For example, a flaw in the design may arise over the course of your study.

Changing the design of the study may lead to the choice of a different method. In turn, this may lead to subsequent changes in the design to accommodate the new method(s).

UX research design

UX research design makes up the plan. It provides the logical structure of any scientific work. It helps you stay on track and systematize the research so to deliver valid data and confidence in decision making based on the results.

Research design functions to ensure the effectiveness and objectivity of your work by providing a blueprint of sorts for the collection, measurement and analysis of the data.

Assumptions and validation in practice: Experiment design

How it uses the assumptions and experiments below:

• Figure out which kind of assumption you have.
• Conduct an experiment like the one listed to see if you assumed correctly.
• If your team did, move forward to the next assumption..
• If they didn’t, evaluate other options.

Assumption 1: We think we have found a problem. Experiment 1 — Online research: Let’s research whether people discuss this problem online. Google, Twitter, and Quora can help. Also check if a solution already exists. Assumption 2: Based on our research, we still think Group X finds this a problem. This group consists of a lot of people, and they all experience the problem. Experiment 2 — Census data and interviews: How many people actually comprise this group? Lead demographic research based on stats and numbers. If this group seems large, talk to some of them in person. See if they all mention the problem. If so, you seem to have proven your point. Assumption 3: We think we have found a solution to this great problem. Experiment 3 — Field research: Now sketch it and talk to some potential users. Then, get out of the building and show it to the target group because we want to make sure they think that your solution will help. If they do, we can move on to the next step.

Assumption 4: We now assume Group X will indeed pay for our solution to their problem. Experiment 4 — Price before Product, Period: Ask potential customers how much they’d pay for this solution, if anything. If they do, figure out if we can actually make it happen.

Assumption 5: We find the solution feasible. Experiment 5 — Feasibility testing: Chat with your engineers/devs. What do they think about building it? Establish if they find it not super hard to do. They will likely appreciate getting involved early on. Assumption 6: We think adding Extra Feature Z will add a lot of value to our solution. Experiment 6 — A/B testing with a mockup: Go and interview users to find out whether the feature makes its inclusion critical. Perhaps create a landing page with and without the feature listed and look at conversion. Don’t ask users if they’ll miss it; show them the product without it and check if they complain. (Useful tools: Invision, UserTesting.com, or AlphaHQ) Assumption 7: We think people use what we designed to solve the problem. Experiment 7  — Usability testing with prototypes: Create a paper or clickable mock and ask users to complete the task. Better yet, just see what happens without any prompt. Invision, UserTesting.com, AlphaHQ, Validately can help you out. Assumption 8 : We think we can build this in Time Period Y. Experiment 8 — Project length estimation: At this point, get more people on board. First, get the engineers into a room, breaking down the product into high-level flows and features. Have them provide high-level point estimates (difficulty: 1-5 points) or T-shirt sizes (difficulty: S, M, L, XL) to get a better overview of how complex your product idea winds up, and how long it would take to build. Assumption 9: Based on what we know, we think the product is running on the right track. Experiment 9 — User testing: The time has come to involve some real users in the process. Talk to some customers about whether they value it enough to actually pay for. Assumption 10: We think we might have reached the stage to kick it all off and launch. Experiment 10 — Prepare the battlefield: Test the product within the organization. Ask the marketing and sales departments whether they all have what they need. A launch roadmap might also help. Here, we’re checking for internal feasibility and how it will all fit the given timeframe. Assumption 11: We assume people will use the product we’re launching. Experiment 11 — Setting up analytics: Setting up Google Analytics, Hotjar, Heap.io and/or other tracking tools. Set these up before launch. Assumption 12: We assume people use our product to solve the problem. Experiment 12 — Ask your customers: Go back to your target users and see how they use the tool you’ve built. Talk to random other users about what they use it for. You may learn of an additional market. Assumption 13: We might miss another feature that we think might work. Experiment 13 : Return to Assumption 6 .

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What is UX Research: The Ultimate Guide for UX Researchers

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Essential elements of an effective UX research plan (examples + templates)

Conducting UX research without a plan is like moving to another country without knowing the language—confusing and exhausting.

To avoid wasting time and resources, it’s crucial to set achievable research goals and work on developing a research plan that’s clear, comprehensive, and aligned with your overarching business goals and research strategy.

A good UX research plan sets out the parameters for your research, and guides how you’ll gather insights to inform product development. In this chapter, we share a step-by-step guide to creating a research plan, including templates and tactics for you to try. You’ll also find expert tips from Paige Bennett, Senior User Research Manager at Affirm, and Sinéad Davis Cochrane, Research Manager at Workday.

What is a UX research plan?

A UX research plan—not to be confused with a UX research strategy or research design—is a plan to guide individual user experience (UX) research projects.

It's a living document that includes a detailed explanation of tactics, methods, timeline, scope, and task owners. It should be co-created and shared with key stakeholders, so everyone is familiar with the project plan, and product teams can meet strategic goals.

A UX research plan is different to a research strategy and research design in both its purpose and contents. Let’s take a look.

Research plan vs. research design vs. research strategy: What’s the difference?

While your UX research plan should be based on strategy, it’s not the same thing. Your UX strategy is a high-level document that contains goals, budget, vision, and expectations. Meanwhile, a plan is a detailed document explaining how the team will achieve those strategic goals. Research design is the form your research itself takes.

In short, a strategy is a guide, a plan is what drives action, and design is the action itself.

What are the benefits of using a UX research plan?

Conducting research without goals and parameters is aimless. A UX research plan is beneficial for your product, user, and business—by building a plan for conducting UX research, you can:

Streamline processes and add structure

Work toward specific, measurable goals, align and engage stakeholders, save time by avoiding rework.

The structure of a research plan allows you to set timelines, expectations, and task owners, so everyone on your team is aligned and empowered to make decisions. Since there’s no second guessing what to do next or which methods to use, you’ll find your process becomes simpler and more efficient. It’s also worth standardizing your process to turn your plan into a template that you can reuse for future projects.

When you set research goals based on strategy, you’ll find it easier to track your team’s progress and keep the project in scope, on time, and on budget. With a solid, strategy-based UX research plan you can also track metrics at different stages of the project and adjust future tactics to get better research findings.

“It’s important to make sure your stakeholders are on the same page with regards to scope, timeline, and goals before you start," explains Paige Bennett, Senior User Research Manager at Affirm. That's because, when stakeholders are aligned, they're much more likely to sign off on product changes that result from UX research.

A written plan is a collaborative way to involve stakeholders in your research and turn them into active participants rather than passive observers. As they get involved, they'll make useful contributions and get a better understanding of your goals.

A UX research plan helps you save time and money quite simply because it’s easier and less expensive to make design or prototype changes than it is to fix usability issues once the product is coded or fully launched. Additionally, having a plan gives your team direction, which means they won’t be conducting research and talking to users without motive, and you’ll be making better use of your resources. What’s more, when everyone is aligned on goals, they’re empowered to make informed decisions instead of waiting for their managers’ approval.

What should a UX research plan include?

In French cuisine, the concept of mise en place—putting in place—allows chefs to plan and set up their workspace with all the required ingredients before cooking. Think of your research plan like this—laying out the key steps you need to go through during research, to help you run a successful and more efficient study.

Here’s what you should include in a UX research plan:

• A brief reminder of the strategy and goals
• An outline of the research objectives
• The purpose of the plan and studies
• A short description of the target audience, sample size, scope, and demographics
• A detailed list of expectations including deliverables, timings, and type of results
• An overview of the test methods and a short explanation of why you chose them
• The test set up or guidelines to outline everything that needs to happen before the study: scenarios, screening questions, and duration of pilot tests
• Your test scripts, questions to ask, or samples to follow
• When and how you’ll present the results
• Cost estimations or requests to go over budget

Collect all UX research findings in one place

Use Maze to run quantitative and qualitative research, influence product design, and shape user-centered products.

How to create a UX research plan

Now we’ve talked through why you need a research plan, let’s get into the how. Here’s a short step-by-step guide on how to write a research plan that will drive results.

• Define the problem statement
• Get stakeholders’ buy-in
• Identify your objectives
• Choose the right research method
• Recruit participants
• Prepare the brief
• Establish the timeline
• Decide how you’ll present your findings

1. Define the problem statement

One of the most important purposes of a research plan is to identify what you’re trying to achieve with the research, and clarify the problem statement. For Paige Bennett , Senior User Research Manager at Affirm, this process begins by sitting together with stakeholders and looking at the problem space.

“We do an exercise called FOG, which stands for ‘Fact, Observation, Guess’, to identify large gaps in knowledge,” says Paige. “Evaluating what you know illuminates questions you still have, which then serves as the foundation of the UX research project.”

You can use different techniques to identify the problem statement, such as stakeholder interviews, team sessions, or analysis of customer feedback. The problem statement should explain what the project is about—helping to define the research scope with clear deliverables and objectives.

2. Identify your objectives

Research objectives need to align with the UX strategy and broader business goals, but you also need to define specific targets to achieve within the research itself—whether that’s understanding a specific problem, or measuring usability metrics . So, before you get into a room with your users and customers, “Think about the research objectives: what you’re doing, why you’re doing it, and what you expect from the UX research process ,” explains Sinéad Davis Cochrane , Research Manager at Workday.

Examples of research objectives might be:

• Learn at what times users interact with your product
• Understand why users return (or not) to your website/app
• Discover what competitor products your users are using
• Uncover any pain points or challenges users find when navigating with your product
• Gauge user interest in and prioritize potential new features

A valuable purpose of setting objectives is ensuring your project doesn't suffer from scope creep. This can happen when stakeholders see your research as an opportunity to ask any question. As a researcher , Sinéad believes your objectives can guide the type of research questions you ask and give your research more focus. Otherwise, anything and everything becomes a research question—which will confuse your findings and be overwhelming to manage.

Sinéad shares a list of questions you should ask yourself and the research team to help set objectives:

• What are you going to do with this information?
• What decisions is it going to inform?
• How are you going to leverage these insights?

Another useful exercise to help identify research objectives is by asking questions that help you get to the core of a problem. Ask these types of questions before starting the planning process:

• Who are the users you’re designing this for?
• What problems and needs do they have?
• What are the pain points of using the product?
• Why are they not using a product like yours?

3. Get stakeholders buy-in

It’s good practice to involve stakeholders at early stages of plan creation to get everyone on board. Sharing your UX research plan with relevant stakeholders means you can gather context, adjust based on comments, and gauge what’s truly important to them. When you present the research plan to key stakeholders, remember to align on the scope of research, and how and when you’ll get back to them with results.

Stakeholders usually have a unique vision of the product, and it’s crucial that you’re able to capture it early on—this doesn’t mean saying yes to everything, but listening to their ideas and having a conversation. Seeing the UX research plan as a living document makes it much easier to edit based on team comments. Plus, the more you listen to other ideas, the easier it will be to evangelize research and get stakeholder buy-in by helping them see the value behind it.

I expect my stakeholders to be participants, and I outline how I expect that to happen. That includes observing interviews, participating in synthesis exercises, or co-presenting research recommendations.

Paige Bennett , Senior User Research Manager at Affirm

4. Choose the right research method

Choose between the different UX research methods to capture different insights from users.

To define the research methods you’ll use, circle back to your research objectives, what stage of the product development process you’re in, and the constraints, resources, and timeline of the project. It’s good research practice to use a mix of different methods to get a more complete perspective of users’ struggles.

For example, if you’re at the start of the design process, a generative research method such as user interviews or field studies will help you generate new insights about the target audience. Or, if you need to evaluate how a new design performs with users, you can run usability tests to get actionable feedback.

It’s also good practice to mix methods that drive quantitative and qualitative results so you can understand context, and catch the user sentiment behind a metric. For instance, if during a remote usability test, you hear a user go ‘Ugh! Where’s the sign up button?’ you’ll get a broader perspective than if you were just reviewing the number of clicks on the same test task.

Examples of UX research methods to consider include:

• Five-second testing
• User interviews
• Field studies
• Card sorting
• Tree testing
• Focus groups
• Usability testing
• Diary studies
• Live website testing

Check out our top UX research templates . Use them as a shortcut to get started on your research.

5. Determine how to recruit participants

Every research plan should include information about the participants you need for your study, and how you’ll recruit them. To identify your perfect candidate, revisit your goals and the questions that need answering, then build a target user persona including key demographics and use cases. Consider the resources you have available already, by asking yourself:

• Do you have a user base you can tap into to collect customer insights ?
• Do you need to hire external participants?
• What’s your budget to recruit users?
• How many users do you need to interact with?

When selecting participants, make sure they represent all your target personas. If different types of people will be using a certain product, you need to make sure that the people you research represent these personas. This means not just being inclusive in your recruitment, but considering secondary personas—the people who may not be your target user base, but interact with your product incidentally.

You should also consider recruiting research participants to test the product on different devices. Paige explains: “If prior research has shown that behavior differs greatly between those who use a product on their phone versus their tablet, I need to better understand those differences—so I’m going to make sure my participants include people who have used a product on both devices.”

During this step, make sure to include information about the required number of participants, how you’ll get them to participate, and how much time you need per user. The main ways to recruit testers are:

• Using an online participant recruitment tool like Maze Panel
• Putting out physical or digital adverts in spaces that are relevant to your product and user
• Reaching out to existing users
• Using participants from previous research
• Recruiting directly from your website or app with a tool like In-Product Prompts

5.1. Determine how you’ll pay them

You should always reward your test participants for their time and insights. Not only because it’s the right thing to do, but also because if they have an incentive they’re more likely to give you complete and insightful answers. If you’re hosting the studies in person, you’ll also need to cover your participants' travel expenses and secure a research space. Running remote moderated or unmoderated research is often considered to be less expensive and faster to complete.

If you’re testing an international audience, remember to check your proposed payment system works worldwide—this might be an Amazon gift card or prepaid Visa cards.

6. Prepare the brief

The next component of a research plan is to create a brief or guide for your research sessions. The kind of brief you need will vary depending on your research method, but for moderated methods like user interviews, field studies, or focus groups, you’ll need a detailed guide and script. The brief is there to remind you which questions to ask and keep the sessions on track.

Your script should cover:

• Introduction: A short message you’ll say to participants before the session begins. This works as a starting point for conversations and helps set the tone for the meeting. If you’re testing without a moderator, you should also include an introductory message to explain what the research is about and the type of answers they should give (in terms of length and specificity).
• Interview questions: Include your list of questions you’ll ask participants during the sessions. These could be examples to help guide the interviews, specific pre-planned questions, or test tasks you’ll ask participants to perform during unmoderated sessions.
• Outro message: Outline what you'll say at the end of the session, including the next steps, asking participants if they are open to future research, and thanking them for their time. This can be a form you share at the end of asynchronous sessions.

It’s crucial you remember to ask participants for their consent. You should do this at the beginning of the test by asking if they’re okay with you recording the session. Use this space to lay out any compensation agreements as well. Then, ask again at the end of the session if they agree with you keeping the results and using the data for research purposes. If possible, explain exactly what you’ll do with their data. Double check and get your legal team’s sign-off on these forms.

7. Establish the timeline

Next in your plan, estimate how long the research project will take and when you should expect to review the findings. Even if not exact, determining an approximate timeline (e.g., two-three weeks) will enable you to manage stakeholders’ expectations of the process and results.

Many people believe UX research is a lengthy process, so they skip it. When you set up a timeline and get stakeholders aligned with it, you can debunk assumptions and put stakeholders’ minds at ease. Plus, if you’re using a product discovery tool like Maze, you can get answers to your tests within days.

8. Decide how you’ll present your findings

When it comes to sharing your findings with your team, presentation matters. You need to make a clear presentation and demonstrate how user insights will influence design and development. If you’ve conducted UX research in the past, share data that proves how implementing user insights has improved product adoption.

Examples of ways you can present your results include:

• A physical or digital PDF report with key statistics and takeaways
• An interactive online report of the individual research questions and their results
• A presentation explaining the results and your findings
• A digital whiteboard, like Miro, to display the results

In your plan, mention how you’ll share insights with the product team. For example, if you’re using Maze, you can start by emailing everyone the ready-to-share report and setting up a meeting with the team to identify how to bring those insights to life. This is key, because your research should be the guiding light for new products or updates, if you want to keep development user-centric. Taking care over how you present your findings will impact whether they’re taken seriously and implemented by other stakeholders.

Your UX research plan template: Free template + example

Whether you’re creating the plan yourself or delegating to your team, a clear UX research plan template cuts your prep time in half.

Find our customizable free UX research plan template here , and keep reading for a filled-in example.

Example: Improving user adoption of a project management tool called Flows

Now, let’s go through how to fill out this template and create a UX research plan with an example.

Executive summary:

Flows aims to increase user adoption and tool engagement by 30% within the next 12 months. Our B2B project management software has been on the market for 3 years and has 25,000 active users across various industries.

By researching the current product experience with existing users, we’ll learn what works and what doesn’t in order to make adjustments to the product and experience.

Research objectives:

Purpose of the plan and studies:.

The purpose is to gather actionable insights into user needs, behaviors, and challenges to inform updates that will drive increased adoption and engagement of 30% for the B2B project management tool within 12 months.

Target audience, sample size, scope, and demographics:

Expectations, deliverables, timings, and type of results:, research methodologies:.

*Some teams will take part in more than one research session.

Research analysis methods:

We are doing a mixed methods study.

User interviews are our primary method for gathering qualitative data, and will be analyzed using thematic analysis .

• Quantitative data will be pulled from usability tests to evaluate the effectiveness of our current design.
• Research set up and guidelines:
• Create baselines surveys to gauge current usage and pain points
• Develop interview/discussion guides and usability testing scenarios
• Pilot test materials with two teams
• User interviews: 60 mins, semi-structured; usability tests: 90 mins
• Findings will be presented in a research report for all stakeholders

Research scripts, questions, and samples:

User interview questions:

• What’s your experience with Flows?
• How does Flows fit into your workflow?
• What is your understanding of Flows’ features?
• What do you wish Flows could do that it currently doesn’t?

Usability test sample with Maze:

Cost estimations or budget requests/pricing:

Total estimated budget: $8,000

More free customizable templates for UX research

Whether you’re creating the plan yourself or are delegating this responsibility to your team, here are six research templates to get started:

• UX research plan template : This editable Miro research project plan example helps you brainstorm user and business-facing problems, objectives, and questions
• UX research brief : You need a clear brief before you conduct UX research—Milanote shares a template that will help you simplify the writing process
• User testing synthesis : Trello put together a sample board to organize user testing notes—you can use this as a guide, but change the titles to fit your UX research purposes
• Usability testing templates : At Maze, we’ve created multiple templates for conducting specific UX research methods—this list will help you create different remote usability tests
• Information architecture (IA) tests template : The way you organize the information in your website or app can improve or damage the user experience—use this template to run IA tests easily
• Feedback survey templates : Ask users anything through a survey, and use these templates to get creative and simplify creation

Everything you need to know about UX research plans

We all know that a robust plan is essential for conducting successful UX research. But, in case you want a quick refresher on what we’ve covered:

• Using a UX research strategy as a starting point will make your plan more likely to succeed
• Determine your research objectives before anything else
• Use a mix of qualitative and quantitative research methods
• Come up with clear personas so you can recruit and test a group of individuals that’s representative of your real end users
• Involve stakeholders from the beginning to get buy-in
• Be vocal about timelines, budget, and expected research findings
• Use the insights to power your product decisions and wow your users; building the solution they genuinely want and need

UX research can happen at any stage of the development lifecycle. When you build products with and for users, you need to include them continuously at various stages of the process.

It’s helpful to explore the need for continuous discovery in your UX research plan and look for a tool like Maze that simplifies the process for you. We’ll cover more about the different research methods and UX research tools in the upcoming chapters—ready to go?

Elevate your UX research workflow

Discover how Maze can streamline and operationalize your research plans to drive real product innovation while saving on costs.

Frequently asked questions

What’s the difference between a UX research plan and a UX research strategy?

The difference between a UX research plan and a UX research strategy is that they cover different levels of scope and detail. A UX research plan is a document that guides individual user experience (UX) research projects. UX research plans are shared documents that everyone on the product team can and should be familiar with. A UX research strategy, on the other hand, outlines the high-level goals, expectations, and demographics of the organization’s approach to research.

What should you include in a user research plan?

Here’s what to include in a user research plan:

• Problem statement
• Research objectives
• Research methods
• Participants' demographics
• Recruitment plan
• User research brief
• Expected timeline
• How to present findings

How do you write a research plan for UX design?

Creating a research plan for user experience (UX) requires a clear problem statement and objectives, choosing the right research method, recruiting participants and briefing them, and establishing a timeline for your project. You'll also need to plan how you'll analyze and present your findings.

How do you plan a UX research roadmap?

To plan a UX research roadmap, start by identifying key business goals and user needs. Align research activities with product milestones to ensure timely insights. Prioritize research methods—like surveys, interviews, and usability tests—based on the project phase and objectives. Set clear timelines and allocate resources accordingly. Regularly update stakeholders on progress and integrate feedback to refine the roadmap continuously.

Generative Research: Definition, Methods, and Examples

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User Research

Research Plan

Explore real examples of Research Plans

Learn how the best operators in tech think about research plans. explore 34 examples of research plans so you never have to start from scratch..

Updated May 1, 2024

What is a Research Plan?

A research plan outlines the objectives, methodology, timeline, and resources needed for a research project, acting as a roadmap for systematic investigation. Reforge can enhance the development and execution of research plans with tools and strategies that streamline each phase, from data collection to analysis and interpretation. Our resources help researchers define clear scopes and approaches, improve efficiency, and effectively communicate with stakeholders.

Popular Research Plan examples

Explore 10 most popular Research Plans from top companies.

Jobs to be Done User Research Guide at Replit

by Tabish Gilani , Created as Head of Product (Director) @ Replit

I created this to teach others how we conducted user interviews at Replit using a Jobs To Be Done (JTBD) framework. It covers everything from how to source, prep, and conduct user interviews to get the most learnings.

• Tabish created this User Research Guide to teach others at Replit how to conduct interviews using a Jobs To Be Done (JTBD) framework.
• It covers everything from how to source, prep, and conduct user interviews to get the most learnings.
• Segmenting users by behavior, demographics, geography, and psychographics is important for targeted research.
• The interview process involves active listening, open-ended questions, and avoiding leading questions.

Automated user feedback collection at Reforge

by Dan Wolchonok , Created as Head of Data @ Reforge

This feedback collection process for analyzing customer input helped me get the people's attention, re-prioritize roadmaps, and rally the team around how to allocate resources for work on a payments page.

• Dan used a feedback collection process to analyze customer input and improve metrics.
• The process involved emailing targeted users, responding to feedback, and aggregating it in a spreadsheet.
• The feedback helped Dan and his team understand why conversion metrics were weakening and make improvements to their product.

Customer Discovery Interview Log at Chegg

by Jack McDermott , Created as Senior Manager, Growth @ Chegg

I used this interview log to get a better sense of our target users, their motivations, and their willingness to pay.

• Jack describes how he communicates the goals of research to participants and internal team members.
• He emphasizes the importance of identifying key business questions and avoiding spreading oneself too thin.
• Jack recommends using visual mediums to display customer insights and elevating representative quotes to drive points home.

Design Methods Research Planning for Facebook Audience Insights

by Behzod Sirjani , Created as Senior User Experience Researcher @ Facebook

I used this framework to design a creative research approach that would get us the evidence we needed to redesign the Audience Insights tool.

• Behzod redesigned the Audience Insights tool, which was underloved and needed a redesign.
• They used a qualitative and fun approach to understand how and why people wanted to use the tool.
• Rolling recruitment was used to reach out to high-value customers and learn in as many directions as possible.

User Research Brief at Clover

by Monil Shah , Created as Lead Product Manager - Subscription Growth and Monetization @ Clover

At Clover the PM uses this template to provide details to their Designer or User Researcher and then design their study based on input they gather here.

• Monil's team used this template to guide their user research for a new subscription product.
• The research helped inform product decisions and influence stakeholders.
• Monil discusses how the team made tradeoff decisions about how to conduct research studies and aimed to move towards continuous discovery.

Enterprise App Management Research - Discussion Guide at Slack

by Behzod Sirjani , Created as Head of Research Operations @ Slack

In order to better understand how to improve the experience of managing Slack at scale, I created this discussion guide to conducted interviews with Slack Admins at our largest customers.

• Behzod created a discussion guide to conduct interviews with Slack Admins at large customers.
• The guide includes warm-up questions, context questions, and core questions about managing Slack.
• The guide emphasizes the importance of building rapport, getting consent to record, and asking open-ended questions.

Power Users Research at Lemon.io

by Lisa Dziuba , Created as Head of Growth Product Marketing @ Lemon.io

When I started the power user research at Lemon.io, my first action was to create a well-structured research project. The research project (research plan) is a must-have PMM document that focuses all the research efforts on the business outcomes. The beauty of this document lies in its ability to keep research on track and be a repository of all docs and tasks, as well as one source of truth.

• Lisa defined objectives for Power Users Research tailored to Lemon.io's company OKRs and gathered input from the founders and team.
• User research relies on collaboration across customer-facing teams and requires a well-structured research project to keep research on track.
• The Power Users Research project involved gathering insights from customer-facing teams on user data since 2015, searching 56 data points, and forming meaningful insights to drive revenue growth.

Feedback River Process at Toptal

by Paul Timmermann , Created as Senior Director of Product @ Toptal

This artifact outlines how we actively use feedback rivers at Toptal to continue to deliver the best experience for our talent and clients.

• This artifact outlines how Paul's team actively uses feedback rivers at Toptal to continue to deliver the best experience for both talent and clients.
• The Feedback River process involves consistently reviewing customer feedback and adding structured insights using tools like Slack and Google Sheets.
• The Feedback River provides a consistent pulse on user sentiments, captures valuable insights, and seamlessly integrates workflows to inform future decision-making processes.

Audience strategy research sprint at Heap

by Shelly Eisen-Livneh , Created as Sr. Product Marketing Manager - Solutions & Audience Strategy @ Heap

At Heap we conducted this lean research approach to help inform GTM/product roadmap iterations.

• Shelly conducted lean research to inform GTM/product roadmap iterations at Heap.
• The research included short-term and long-term GTM recommendations for two personas.
• The research also analyzed org structures, buying committee roles, and updated messaging for the personas.

Automatically scheduling customer interviews at VEED

by Thomas Christensen , Created as Senior Product Manager, Growth @ VEED.IO

At Veed we book customer interview sessions based on user behavior by automatically filtering to a set of users, emailing them, and booking time across the team's shared availability.

• Thomas and the activation team at Veed.io conducted continuous user interviews to improve the initial experience with Veed.
• They aimed to speak with three types of users: habit users, "aha" users, and new users who didn't make their first video.
• They used existing tools like Amplitude, customer.io, Calendly, Zapier, and Notion to automate the process of booking and documenting user research sessions.

View all 34 examples of Research Plans.

Other popular Research Plans

Explore the full catalog of Research Plan resources to get inspired

Behzod Sirjani, Created as Head of Research Operations @ Slack

Lisa Dziuba, Created as Head of Growth Product Marketing @ Lemon.io

Paul Timmermann, Created as Senior Director of Product @ Toptal

Shelly Eisen-Livneh, Created as Sr. Product Marketing Manager - Solutions & Audience Strategy @ Heap

Thomas Christensen, Created as Senior Product Manager, Growth @ VEED.IO

Daniel Andor, Created as Product Design and Strategy Specialist | Founder @ Durran

This is the framework I use to walk clients through a thorough product discovery process, setting up the entire remaining development of the product.

Olena Avramenko, Created as Head of Product (Interim) @ Sitly

I created this research plan to understand how parents in different markets search and select babysitters on Sitly.

Ali Riehle, Created as Lead Product Designer @ Reforge

I created this diagram to help communicate and visualize our team’s cross-functional research approach across about two weeks.

Kanika Tibrewala, Created as UX Design and Research Lead @ Swiggy

We used this framework to launch our platform in just 9 days! It helped us clearly see where we were succeeding, failing, and what we needed to do next.

Behzod Sirjani, Created as Founder @ Yet Another Studio

I created this discussion guide and email copy for an early stage company who wanted to better understand developer workflows to inform a tool they were building.

View all 34 examples of Research Plans

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Explore artifacts of all kinds

Browse other popular categories to get ideas for your own work

Go deeper with step by step guides

Recruit the right research participants

By Behzod Sirjani

Recruiting participants is a critical part of having an effective customer conversation, but too many people recruit out of convenience rather than conviction. This unit will cover a straightforward process that will enable you to recruit the right people to have the right conversations.

Determine conversation logistics

Intentionally choosing who joins a customer conversation and where/how it occurs will significantly impact the quality. This unit explores the tradeoffs and highlights important considerations in making those decisions.

Conduct user interviews to refine user value

By Anand Subramani & Jiaona Zhang

User interviews are a foundational tool for product managers.

They provide rich provide qualitative data to help you refine feature hypotheses and dive deep into the problems users are facing. Understanding problems from a user’s perspective ensures you’re building features that deliver true value to users instead of operating on assumptions.

However, effective user interviews require careful planning, execution, and analysis. In this guide, you'll learn the steps to effective user interviews.

Together, these four steps ensure that you're gathering the right insights about the right users.

• Determine your interview audience to ensure that you are talking to the right users for this specific opportunity
• Recruit users from that target audience to speak with you.
• Structure the interview to ensure you ask the right questions for the opportunity. The right questions will get you the right data
• Debrief and synthesize insights to bridge the gap between what users said in the interview and what you need to learn.

At the end of the user interview process, you’ll be able to build a User Value Map .

Create a conversation guide

It’s very easy to ask customers exactly what you want to know, but this is rarely the most effective way to have a conversation. This unit focuses on taking the topics that you want to discuss and turning them into a guide for you to use in your conversation, allowing you to engage your participants more confidently.

I call this a guide and not a script because it’s not something that you have to follow word for word, but it should support you and be there for you when you get lost.

Topics related to Research Plan

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

Last updated

30 January 2024

Reviewed by

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

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

Once you have outlined your goals, objectives, steps, and tasks, it’s time to drill down on selecting research methods . You’ll want to leverage specific research strategies and processes. When you know what methods will help you reach your goals, you and your teams will have direction to perform and execute your assigned tasks.

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

Should you be using a customer insights hub?

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Do you analyze research data?

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Home > Books > Cyberspace

Research Design and Methodology

Submitted: 23 January 2019 Reviewed: 08 March 2019 Published: 07 August 2019

DOI: 10.5772/intechopen.85731

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Edited by Evon Abu-Taieh, Abdelkrim El Mouatasim and Issam H. Al Hadid

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There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design. The second part discusses about qualitative and quantitative data collection methods. The last part illustrates the general research framework. The purpose of this section is to indicate how the research was conducted throughout the study periods.

• research design
• methodology
• data sources

Author Information

Kassu jilcha sileyew *.

• School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia

*Address all correspondence to: [email protected]

1. Introduction

Research methodology is the path through which researchers need to conduct their research. It shows the path through which these researchers formulate their problem and objective and present their result from the data obtained during the study period. This research design and methodology chapter also shows how the research outcome at the end will be obtained in line with meeting the objective of the study. This chapter hence discusses the research methods that were used during the research process. It includes the research methodology of the study from the research strategy to the result dissemination. For emphasis, in this chapter, the author outlines the research strategy, research design, research methodology, the study area, data sources such as primary data sources and secondary data, population consideration and sample size determination such as questionnaires sample size determination and workplace site exposure measurement sample determination, data collection methods like primary data collection methods including workplace site observation data collection and data collection through desk review, data collection through questionnaires, data obtained from experts opinion, workplace site exposure measurement, data collection tools pretest, secondary data collection methods, methods of data analysis used such as quantitative data analysis and qualitative data analysis, data analysis software, the reliability and validity analysis of the quantitative data, reliability of data, reliability analysis, validity, data quality management, inclusion criteria, ethical consideration and dissemination of result and its utilization approaches. In order to satisfy the objectives of the study, a qualitative and quantitative research method is apprehended in general. The study used these mixed strategies because the data were obtained from all aspects of the data source during the study time. Therefore, the purpose of this methodology is to satisfy the research plan and target devised by the researcher.

2. Research design

The research design is intended to provide an appropriate framework for a study. A very significant decision in research design process is the choice to be made regarding research approach since it determines how relevant information for a study will be obtained; however, the research design process involves many interrelated decisions [ 1 ].

This study employed a mixed type of methods. The first part of the study consisted of a series of well-structured questionnaires (for management, employee’s representatives, and technician of industries) and semi-structured interviews with key stakeholders (government bodies, ministries, and industries) in participating organizations. The other design used is an interview of employees to know how they feel about safety and health of their workplace, and field observation at the selected industrial sites was undertaken.

Hence, this study employs a descriptive research design to agree on the effects of occupational safety and health management system on employee health, safety, and property damage for selected manufacturing industries. Saunders et al. [ 2 ] and Miller [ 3 ] say that descriptive research portrays an accurate profile of persons, events, or situations. This design offers to the researchers a profile of described relevant aspects of the phenomena of interest from an individual, organizational, and industry-oriented perspective. Therefore, this research design enabled the researchers to gather data from a wide range of respondents on the impact of safety and health on manufacturing industries in Ethiopia. And this helped in analyzing the response obtained on how it affects the manufacturing industries’ workplace safety and health. The research overall design and flow process are depicted in Figure 1 .

Research methods and processes (author design).

3. Research methodology

To address the key research objectives, this research used both qualitative and quantitative methods and combination of primary and secondary sources. The qualitative data supports the quantitative data analysis and results. The result obtained is triangulated since the researcher utilized the qualitative and quantitative data types in the data analysis. The study area, data sources, and sampling techniques were discussed under this section.

3.1 The study area

According to Fraenkel and Warren [ 4 ] studies, population refers to the complete set of individuals (subjects or events) having common characteristics in which the researcher is interested. The population of the study was determined based on random sampling system. This data collection was conducted from March 07, 2015 to December 10, 2016, from selected manufacturing industries found in Addis Ababa city and around. The manufacturing companies were selected based on their employee number, established year, and the potential accidents prevailing and the manufacturing industry type even though all criterions were difficult to satisfy.

3.2 Data sources

3.2.1 primary data sources.

It was obtained from the original source of information. The primary data were more reliable and have more confidence level of decision-making with the trusted analysis having direct intact with occurrence of the events. The primary data sources are industries’ working environment (through observation, pictures, and photograph) and industry employees (management and bottom workers) (interview, questionnaires and discussions).

3.2.2 Secondary data

Desk review has been conducted to collect data from various secondary sources. This includes reports and project documents at each manufacturing sectors (more on medium and large level). Secondary data sources have been obtained from literatures regarding OSH, and the remaining data were from the companies’ manuals, reports, and some management documents which were included under the desk review. Reputable journals, books, different articles, periodicals, proceedings, magazines, newsletters, newspapers, websites, and other sources were considered on the manufacturing industrial sectors. The data also obtained from the existing working documents, manuals, procedures, reports, statistical data, policies, regulations, and standards were taken into account for the review.

In general, for this research study, the desk review has been completed to this end, and it had been polished and modified upon manuals and documents obtained from the selected companies.

4. Population and sample size

4.1 population.

The study population consisted of manufacturing industries’ employees in Addis Ababa city and around as there are more representative manufacturing industrial clusters found. To select representative manufacturing industrial sector population, the types of the industries expected were more potential to accidents based on random and purposive sampling considered. The population of data was from textile, leather, metal, chemicals, and food manufacturing industries. A total of 189 sample sizes of industries responded to the questionnaire survey from the priority areas of the government. Random sample sizes and disproportionate methods were used, and 80 from wood, metal, and iron works; 30 from food, beverage, and tobacco products; 50 from leather, textile, and garments; 20 from chemical and chemical products; and 9 from other remaining 9 clusters of manufacturing industries responded.

4.2 Questionnaire sample size determination

A simple random sampling and purposive sampling methods were used to select the representative manufacturing industries and respondents for the study. The simple random sampling ensures that each member of the population has an equal chance for the selection or the chance of getting a response which can be more than equal to the chance depending on the data analysis justification. Sample size determination procedure was used to get optimum and reasonable information. In this study, both probability (simple random sampling) and nonprobability (convenience, quota, purposive, and judgmental) sampling methods were used as the nature of the industries are varied. This is because of the characteristics of data sources which permitted the researchers to follow the multi-methods. This helps the analysis to triangulate the data obtained and increase the reliability of the research outcome and its decision. The companies’ establishment time and its engagement in operation, the number of employees and the proportion it has, the owner types (government and private), type of manufacturing industry/production, types of resource used at work, and the location it is found in the city and around were some of the criteria for the selections.

The determination of the sample size was adopted from Daniel [ 5 ] and Cochran [ 6 ] formula. The formula used was for unknown population size Eq. (1) and is given as

where n  = sample size, Z  = statistic for a level of confidence, P  = expected prevalence or proportion (in proportion of one; if 50%, P  = 0.5), and d  = precision (in proportion of one; if 6%, d  = 0.06). Z statistic ( Z ): for the level of confidence of 95%, which is conventional, Z value is 1.96. In this study, investigators present their results with 95% confidence intervals (CI).

The expected sample number was 267 at the marginal error of 6% for 95% confidence interval of manufacturing industries. However, the collected data indicated that only 189 populations were used for the analysis after rejecting some data having more missing values in the responses from the industries. Hence, the actual data collection resulted in 71% response rate. The 267 population were assumed to be satisfactory and representative for the data analysis.

4.3 Workplace site exposure measurement sample determination

The sample size for the experimental exposure measurements of physical work environment has been considered based on the physical data prepared for questionnaires and respondents. The response of positive were considered for exposure measurement factors to be considered for the physical environment health and disease causing such as noise intensity, light intensity, pressure/stress, vibration, temperature/coldness, or hotness and dust particles on 20 workplace sites. The selection method was using random sampling in line with purposive method. The measurement of the exposure factors was done in collaboration with Addis Ababa city Administration and Oromia Bureau of Labour and Social Affair (AACBOLSA). Some measuring instruments were obtained from the Addis Ababa city and Oromia Bureau of Labour and Social Affair.

5. Data collection methods

Data collection methods were focused on the followings basic techniques. These included secondary and primary data collections focusing on both qualitative and quantitative data as defined in the previous section. The data collection mechanisms are devised and prepared with their proper procedures.

5.1 Primary data collection methods

Primary data sources are qualitative and quantitative. The qualitative sources are field observation, interview, and informal discussions, while that of quantitative data sources are survey questionnaires and interview questions. The next sections elaborate how the data were obtained from the primary sources.

5.1.1 Workplace site observation data collection

Observation is an important aspect of science. Observation is tightly connected to data collection, and there are different sources for this: documentation, archival records, interviews, direct observations, and participant observations. Observational research findings are considered strong in validity because the researcher is able to collect a depth of information about a particular behavior. In this dissertation, the researchers used observation method as one tool for collecting information and data before questionnaire design and after the start of research too. The researcher made more than 20 specific observations of manufacturing industries in the study areas. During the observations, it found a deeper understanding of the working environment and the different sections in the production system and OSH practices.

5.1.2 Data collection through interview

Interview is a loosely structured qualitative in-depth interview with people who are considered to be particularly knowledgeable about the topic of interest. The semi-structured interview is usually conducted in a face-to-face setting which permits the researcher to seek new insights, ask questions, and assess phenomena in different perspectives. It let the researcher to know the in-depth of the present working environment influential factors and consequences. It has provided opportunities for refining data collection efforts and examining specialized systems or processes. It was used when the researcher faces written records or published document limitation or wanted to triangulate the data obtained from other primary and secondary data sources.

This dissertation is also conducted with a qualitative approach and conducting interviews. The advantage of using interviews as a method is that it allows respondents to raise issues that the interviewer may not have expected. All interviews with employees, management, and technicians were conducted by the corresponding researcher, on a face-to-face basis at workplace. All interviews were recorded and transcribed.

5.1.3 Data collection through questionnaires

The main tool for gaining primary information in practical research is questionnaires, due to the fact that the researcher can decide on the sample and the types of questions to be asked [ 2 ].

In this dissertation, each respondent is requested to reply to an identical list of questions mixed so that biasness was prevented. Initially the questionnaire design was coded and mixed up from specific topic based on uniform structures. Consequently, the questionnaire produced valuable data which was required to achieve the dissertation objectives.

The questionnaires developed were based on a five-item Likert scale. Responses were given to each statement using a five-point Likert-type scale, for which 1 = “strongly disagree” to 5 = “strongly agree.” The responses were summed up to produce a score for the measures.

5.1.4 Data obtained from experts’ opinion

The data was also obtained from the expert’s opinion related to the comparison of the knowledge, management, collaboration, and technology utilization including their sub-factors. The data obtained in this way was used for prioritization and decision-making of OSH, improving factor priority. The prioritization of the factors was using Saaty scales (1–9) and then converting to Fuzzy set values obtained from previous researches using triangular fuzzy set [ 7 ].

5.1.5 Workplace site exposure measurement

The researcher has measured the workplace environment for dust, vibration, heat, pressure, light, and noise to know how much is the level of each variable. The primary data sources planned and an actual coverage has been compared as shown in Table 1 .

Planned versus actual coverage of the survey.

The response rate for the proposed data source was good, and the pilot test also proved the reliability of questionnaires. Interview/discussion resulted in 87% of responses among the respondents; the survey questionnaire response rate obtained was 71%, and the field observation response rate was 90% for the whole data analysis process. Hence, the data organization quality level has not been compromised.

This response rate is considered to be representative of studies of organizations. As the study agrees on the response rate to be 30%, it is considered acceptable [ 8 ]. Saunders et al. [ 2 ] argued that the questionnaire with a scale response of 20% response rate is acceptable. Low response rate should not discourage the researchers, because a great deal of published research work also achieves low response rate. Hence, the response rate of this study is acceptable and very good for the purpose of meeting the study objectives.

5.1.6 Data collection tool pretest

The pretest for questionnaires, interviews, and tools were conducted to validate that the tool content is valid or not in the sense of the respondents’ understanding. Hence, content validity (in which the questions are answered to the target without excluding important points), internal validity (in which the questions raised answer the outcomes of researchers’ target), and external validity (in which the result can generalize to all the population from the survey sample population) were reflected. It has been proved with this pilot test prior to the start of the basic data collections. Following feedback process, a few minor changes were made to the originally designed data collect tools. The pilot test made for the questionnaire test was on 10 sample sizes selected randomly from the target sectors and experts.

5.2 Secondary data collection methods

The secondary data refers to data that was collected by someone other than the user. This data source gives insights of the research area of the current state-of-the-art method. It also makes some sort of research gap that needs to be filled by the researcher. This secondary data sources could be internal and external data sources of information that may cover a wide range of areas.

Literature/desk review and industry documents and reports: To achieve the dissertation’s objectives, the researcher has conducted excessive document review and reports of the companies in both online and offline modes. From a methodological point of view, literature reviews can be comprehended as content analysis, where quantitative and qualitative aspects are mixed to assess structural (descriptive) as well as content criteria.

A literature search was conducted using the database sources like MEDLINE; Emerald; Taylor and Francis publications; EMBASE (medical literature); PsycINFO (psychological literature); Sociological Abstracts (sociological literature); accident prevention journals; US Statistics of Labor, European Safety and Health database; ABI Inform; Business Source Premier (business/management literature); EconLit (economic literature); Social Service Abstracts (social work and social service literature); and other related materials. The search strategy was focused on articles or reports that measure one or more of the dimensions within the research OSH model framework. This search strategy was based on a framework and measurement filter strategy developed by the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Based on screening, unrelated articles to the research model and objectives were excluded. Prior to screening, researcher (principal investigator) reviewed a sample of more than 2000 articles, websites, reports, and guidelines to determine whether they should be included for further review or reject. Discrepancies were thoroughly identified and resolved before the review of the main group of more than 300 articles commenced. After excluding the articles based on the title, keywords, and abstract, the remaining articles were reviewed in detail, and the information was extracted on the instrument that was used to assess the dimension of research interest. A complete list of items was then collated within each research targets or objectives and reviewed to identify any missing elements.

6. Methods of data analysis

Data analysis method follows the procedures listed under the following sections. The data analysis part answered the basic questions raised in the problem statement. The detailed analysis of the developed and developing countries’ experiences on OSH regarding manufacturing industries was analyzed, discussed, compared and contrasted, and synthesized.

6.1 Quantitative data analysis

Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.

Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [ 9 , 10 ]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.

Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.

6.2 Qualitative data analysis

Qualitative data analysis used for triangulation of the quantitative data analysis. The interview, observation, and report records were used to support the findings. The analysis has been incorporated with the quantitative discussion results in the data analysis parts.

6.3 Data analysis software

The data were entered using SPSS 20.0 on Windows 10 and analyzed. The analysis supported with SPSS software much contributed to the finding. It had contributed to the data validation and correctness of the SPSS results. The software analyzed and compared the results of different variables used in the research questionnaires. Excel is also used to draw the pictures and calculate some analytical solutions.

7. The reliability and validity analysis of the quantitative data

7.1 reliability of data.

The reliability of measurements specifies the amount to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument [ 8 ]. In reliability analysis, it has been checked for the stability and consistency of the data. In the case of reliability analysis, the researcher checked the accuracy and precision of the procedure of measurement. Reliability has numerous definitions and approaches, but in several environments, the concept comes to be consistent [ 8 ]. The measurement fulfills the requirements of reliability when it produces consistent results during data analysis procedure. The reliability is determined through Cranach’s alpha as shown in Table 2 .

Internal consistency and reliability test of questionnaires items.

K stands for knowledge; M, management; T, technology; C, collaboration; P, policy, standards, and regulation; H, hazards and accident conditions; PPE, personal protective equipment.

7.2 Reliability analysis

Cronbach’s alpha is a measure of internal consistency, i.e., how closely related a set of items are as a group [ 11 ]. It is considered to be a measure of scale reliability. The reliability of internal consistency most of the time is measured based on the Cronbach’s alpha value. Reliability coefficient of 0.70 and above is considered “acceptable” in most research situations [ 12 ]. In this study, reliability analysis for internal consistency of Likert-scale measurement after deleting 13 items was found similar; the reliability coefficients were found for 76 items were 0.964 and for the individual groupings made shown in Table 2 . It was also found internally consistent using the Cronbach’s alpha test. Table 2 shows the internal consistency of the seven major instruments in which their reliability falls in the acceptable range for this research.

7.3 Validity

Face validity used as defined by Babbie [ 13 ] is an indicator that makes it seem a reasonable measure of some variables, and it is the subjective judgment that the instrument measures what it intends to measure in terms of relevance [ 14 ]. Thus, the researcher ensured, in this study, when developing the instruments that uncertainties were eliminated by using appropriate words and concepts in order to enhance clarity and general suitability [ 14 ]. Furthermore, the researcher submitted the instruments to the research supervisor and the joint supervisor who are both occupational health experts, to ensure validity of the measuring instruments and determine whether the instruments could be considered valid on face value.

In this study, the researcher was guided by reviewed literature related to compliance with the occupational health and safety conditions and data collection methods before he could develop the measuring instruments. In addition, the pretest study that was conducted prior to the main study assisted the researcher to avoid uncertainties of the contents in the data collection measuring instruments. A thorough inspection of the measuring instruments by the statistician and the researcher’s supervisor and joint experts, to ensure that all concepts pertaining to the study were included, ensured that the instruments were enriched.

8. Data quality management

Insight has been given to the data collectors on how to approach companies, and many of the questionnaires were distributed through MSc students at Addis Ababa Institute of Technology (AAiT) and manufacturing industries’ experience experts. This made the data quality reliable as it has been continually discussed with them. Pretesting for questionnaire was done on 10 workers to assure the quality of the data and for improvement of data collection tools. Supervision during data collection was done to understand how the data collectors are handling the questionnaire, and each filled questionnaires was checked for its completeness, accuracy, clarity, and consistency on a daily basis either face-to-face or by phone/email. The data expected in poor quality were rejected out of the acting during the screening time. Among planned 267 questionnaires, 189 were responded back. Finally, it was analyzed by the principal investigator.

9. Inclusion criteria

The data were collected from the company representative with the knowledge of OSH. Articles written in English and Amharic were included in this study. Database information obtained in relation to articles and those who have OSH area such as interventions method, method of accident identification, impact of occupational accidents, types of occupational injuries/disease, and impact of occupational accidents, and disease on productivity and costs of company and have used at least one form of feedback mechanism. No specific time period was chosen in order to access all available published papers. The questionnaire statements which are similar in the questionnaire have been rejected from the data analysis.

10. Ethical consideration

Ethical clearance was obtained from the School of Mechanical and Industrial Engineering, Institute of Technology, Addis Ababa University. Official letters were written from the School of Mechanical and Industrial Engineering to the respective manufacturing industries. The purpose of the study was explained to the study subjects. The study subjects were told that the information they provided was kept confidential and that their identities would not be revealed in association with the information they provided. Informed consent was secured from each participant. For bad working environment assessment findings, feedback will be given to all manufacturing industries involved in the study. There is a plan to give a copy of the result to the respective study manufacturing industries’ and ministries’ offices. The respondents’ privacy and their responses were not individually analyzed and included in the report.

11. Dissemination and utilization of the result

The result of this study will be presented to the Addis Ababa University, AAiT, School of Mechanical and Industrial Engineering. It will also be communicated to the Ethiopian manufacturing industries, Ministry of Labor and Social Affair, Ministry of Industry, and Ministry of Health from where the data was collected. The result will also be availed by publication and online presentation in Google Scholars. To this end, about five articles were published and disseminated to the whole world.

12. Conclusion

The research methodology and design indicated overall process of the flow of the research for the given study. The data sources and data collection methods were used. The overall research strategies and framework are indicated in this research process from problem formulation to problem validation including all the parameters. It has laid some foundation and how research methodology is devised and framed for researchers. This means, it helps researchers to consider it as one of the samples and models for the research data collection and process from the beginning of the problem statement to the research finding. Especially, this research flow helps new researchers to the research environment and methodology in particular.

Conflict of interest

There is no “conflict of interest.”

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A Groundbreaking Scientific Discovery Just Created the Instruction Manual for Light-Speed Travel

In a first for warp drives, this research actually obeys the laws of physics.

If a superluminal—meaning faster than the speed of light—warp drive like Alcubierre’s worked, it would revolutionize humanity’s endeavors across the universe , allowing us, perhaps, to reach Alpha Centauri, our closest star system, in days or weeks even though it’s four light years away.

The clip above from the 2016 film Star Trek Beyond showcases the effect of a starship zipping through space inside a faster-than-light warp bubble. You can see the imagined but hypothetically accurate warping of spacetime.

However, the Alcubierre drive has a glaring problem: the force behind its operation, called “negative energy,” involves exotic particles—hypothetical matter that, as far as we know, doesn’t exist in our universe. Described only in mathematical terms, exotic particles act in unexpected ways, like having negative mass and working in opposition to gravity (in fact, it has “anti-gravity”). For the past 30 years, scientists have been publishing research that chips away at the inherent hurdles to light speed revealed in Alcubierre’s foundational 1994 article published in the peer-reviewed journal Classical and Quantum Gravity .

Now, researchers at the New York City-based think tank Applied Physics believe they’ve found a creative new approach to solving the warp drive’s fundamental roadblock. Along with colleagues from other institutions, the team envisioned a “positive energy” system that doesn’t violate the known laws of physics . It’s a game-changer, say two of the study’s authors: Gianni Martire, CEO of Applied Physics, and Jared Fuchs, Ph.D., a senior scientist there. Their work, also published in Classical and Quantum Gravity in late April, could be the first chapter in the manual for interstellar spaceflight.

Positive energy makes all the difference. Imagine you are an astronaut in space, pushing a tennis ball away from you. Instead of moving away, the ball pushes back, to the point that it would “take your hand off” if you applied enough pushing force, Martire tells Popular Mechanics . That’s a sign of negative energy, and, though the Alcubierre drive design requires it, there’s no way to harness it.

Instead, regular old positive energy is more feasible for constructing the “ warp bubble .” As its name suggests, it’s a spherical structure that surrounds and encloses space for a passenger ship using a shell of regular—but incredibly dense—matter. The bubble propels the spaceship using the powerful gravity of the shell, but without causing the passengers to feel any acceleration. “An elevator ride would be more eventful,” Martire says.

That’s because the density of the shell, as well as the pressure it exerts on the interior, is controlled carefully, Fuchs tells Popular Mechanics . Nothing can travel faster than the speed of light, according to the gravity-bound principles of Albert Einstein’s theory of general relativity . So the bubble is designed such that observers within their local spacetime environment—inside the bubble—experience normal movement in time. Simultaneously, the bubble itself compresses the spacetime in front of the ship and expands it behind the ship, ferrying itself and the contained craft incredibly fast. The walls of the bubble generate the necessary momentum, akin to the momentum of balls rolling, Fuchs explains. “It’s the movement of the matter in the walls that actually creates the effect for passengers on the inside.”

Building on its 2021 paper published in Classical and Quantum Gravity —which details the same researchers’ earlier work on physical warp drives—the team was able to model the complexity of the system using its own computational program, Warp Factory. This toolkit for modeling warp drive spacetimes allows researchers to evaluate Einstein’s field equations and compute the energy conditions required for various warp drive geometries. Anyone can download and use it for free . These experiments led to what Fuchs calls a mini model, the first general model of a positive-energy warp drive. Their past work also demonstrated that the amount of energy a warp bubble requires depends on the shape of the bubble; for example, the flatter the bubble in the direction of travel, the less energy it needs.

☄️ DID YOU KNOW? People have been imagining traveling as fast as light for nearly a century, if not longer. The 1931 novel Islands of Space by John W. Campbell mentions a “warp” method in the context of superluminal space travel.

This latest advancement suggests fresh possibilities for studying warp travel design, Erik Lentz, Ph.D., tells Popular Mechanics . In his current position as a staff physicist at Pacific Northwest National Laboratory in Richland, Washington, Lentz contributes to research on dark matter detection and quantum information science research. His independent research in warp drive theory also aims to be grounded in conventional physics while reimagining the shape of warped space. The topic needs to overcome many practical hurdles, he says.

Controlling warp bubbles requires a great deal of coordination because they involve enormous amounts of matter and energy to keep the passengers safe and with a similar passage of time as the destination. “We could just as well engineer spacetime where time passes much differently inside [the passenger compartment] than outside. We could miss our appointment at Proxima Centauri if we aren’t careful,” Lentz says. “That is still a risk if we are traveling less than the speed of light.” Communication between people inside the bubble and outside could also become distorted as it passes through the curvature of warped space, he adds.

While Applied Physics’ current solution requires a warp drive that travels below the speed of light, the model still needs to plug in a mass equivalent to about two Jupiters. Otherwise, it will never achieve the gravitational force and momentum high enough to cause a meaningful warp effect. But no one knows what the source of this mass could be—not yet, at least. Some research suggests that if we could somehow harness dark matter , we could use it for light-speed travel, but Fuchs and Martire are doubtful, since it’s currently a big mystery (and an exotic particle).

Despite the many problems scientists still need to solve to build a working warp drive, the Applied Physics team claims its model should eventually get closer to light speed. And even if a feasible model remains below the speed of light, it’s a vast improvement over today’s technology. For example, traveling at even half the speed of light to Alpha Centauri would take nine years. In stark contrast, our fastest spacecraft, Voyager 1—currently traveling at 38,000 miles per hour—would take 75,000 years to reach our closest neighboring star system.

Of course, as you approach the actual speed of light, things get truly weird, according to the principles of Einstein’s special relativity . The mass of an object moving faster and faster would increase infinitely, eventually requiring an infinite amount of energy to maintain its speed.

“That’s the chief limitation and key challenge we have to overcome—how can we have all this matter in our [bubble], but not at such a scale that we can never even put it together?” Martire says. It’s possible the answer lies in condensed matter physics, he adds. This branch of physics deals particularly with the forces between atoms and electrons in matter. It has already proven fundamental to several of our current technologies, such as transistors, solid-state lasers, and magnetic storage media.

The other big issue is that current models allow a stable warp bubble, but only for a constant velocity. Scientists still need to figure out how to design an initial acceleration. On the other end of the journey, how will the ship slow down and stop? “It’s like trying to grasp the automobile for the first time,” Martire says. “We don’t have an engine just yet, but we see the light at the end of the tunnel.” Warp drive technology is at the stage of 1882 car technology, he says: when automobile travel was possible, but it still looked like a hard, hard problem.

The Applied Physics team believes future innovations in warp travel are inevitable. The general positive energy model is a first step. Besides, you don’t need to zoom at light speed to achieve distances that today are just a dream, Martire says. “Humanity is officially, mathematically, on an interstellar track.”

Before joining Popular Mechanics , Manasee Wagh worked as a newspaper reporter, a science journalist, a tech writer, and a computer engineer. She’s always looking for ways to combine the three greatest joys in her life: science, travel, and food.

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