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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.

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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

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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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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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

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Repository of ten perfect research question examples will provide you a better perspective about how to create research questions.

Make sure that your selected topic is intriguing, manageable, and relevant. Here are some guidelines to help understand how to find a good dissertation topic.

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

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5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

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

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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Research Design Steps: Comprehensive Guide

Markets are constantly changing, and it’s important to have a sound research plan in place if you want your company or business’ product stand out from the competition. This article will help you understand the 11 steps that need to be followed to execute a sound market research study. This formal process can also be called “Research Design”. 

Table of Contents

11 steps of research design, comprehensive guide, 1. define the research problem or opportunity.

The first step in any research process is to clearly define the research problem or opportunity. This can be done through a number of different methods, including interviews, focus groups , and surveys.

While it may seem like a simple task, defining the research problem or opportunity is crucial to the success of any research project. Without a clear definition, it can be difficult to determine which research methods to use and how to interpret the results.

If you’re not sure where to start, there are a number of resources available to help you define the research problem or opportunity. The following articles offer some helpful tips:

• How to Define a Research Problem or Opportunity
• How to Identify a Research Problem or Opportunity
• How to Write a Problem Statement for Your research Project
• How to Develop a research Questionnaire

Once you’ve taken some time to define the research problem or opportunity, you can move on to the next step in the research process. 

2. Conduct a literature review

Once the research problem has been defined, the next step is to conduct a literature review. This helps to provide a foundation for the study and determine what has already been studied in this area.

A literature review is an important step in conducting research. It helps to define the problem and determine what has already been studied in this area. This process should be unbiased and objective. It should identify gaps in the literature and make suggestions for further research.

The process of reviewing  literature  can be a daunting task, but it is important to remember that it does not need to be exhaustive. The goal is to identify relevant literature and synthesize the information into a cohesive overview.

Tips to conduct a literature review

The following tips will help you conduct a literature review:

• Define your research question before you begin your search. This will help you focus your search and save time.
• Use keyword searching to find relevant articles. Try different combinations of keywords until you find what you are looking for.
• Use databases such as Google Scholar, PubMed, and Web of Science. These databases will help you find peer-reviewed articles.
• Read the abstracts of the articles to determine if they are relevant to your topic. If the abstract is not available, read the full text of the article.
• Organize your literature review using a table or concept map. This will help you see the relationships between different concepts and ideas.
• Write a summary of what you have found in each article. This will help you remember the main points of each article and synthesize the information into a cohesive overview.

Conducting a literature review can seem to be a tedious  task, though it is an important step in conducting research. By following these tips, you can make the literature review process easier and more efficient. Once you have completed your literature review, you will be one step closer to writing your research paper!

3. Develop research objectives (aka Hypothesis)

After conducting the literature review, it is important to develop clear research objectives. This will help guide the rest of the research process and ensure that all steps are aligned with the goals of the study.

There are a few different ways to go about developing research objectives. One approach is to start with the research question, and then develop hypotheses that can be tested through data collection and analysis. Another approach is to think about the overall goal of the research project and what needs to be accomplished in order to achieve that goal.

Whichever approach you choose, it is important to be clear and concise when writing your research objectives. They should be specific enough that they can be measured, but not so specific that they limit the scope of your study. Once you have developed your research objectives, you can use them to guide the rest of your research process.

If you’re stuck on where to start, try brainstorming a list of potential objectives and then narrowing down the list to the most important or relevant ones. You can also consult with your supervisor or other experts in your field to get their input on what objectives would be most appropriate for your research project.

Once you have your research objectives, you can begin thinking about how to operationalize them. This means determining how you will measure the variables that are mentioned in your objectives. For example, if one of your objectives is to examine the relationship between two variables, you will need to decide which type of data collection and analysis methods will be best suited for measuring that relationship.

Operationalizing your research objectives is an important step in ensuring that your study is well-designed and that all of its components are aligned with its overall goals. By taking the time to develop clear and concise research objectives, you can set your study up for success.

4. Formulate your research design

The fourth step is to identify the research design. This will determine the overall approach of the study and include information such as the type of study, the population, and the sampling method.

When formulating your research design, it is important to consider the type of study, the population, and the sampling method. The type of study will determine the overall approach of the research, while the population and sampling method will help to identify the target audience and how best to collect data. By taking all of these factors into consideration, you can develop a well-rounded research design that will be able to address your research question effectively.

There are a variety of different research designs that you can choose from, so it is important to select one that is best suited for your particular study. For example, if you are interested in investigating a specific phenomenon, you may want to choose a case study design. On the other hand, if you are interested in comparing two groups of people, you may want to choose a comparative research design. Once you have selected a research design, you will need to determine the population and sampling method. The population is the group of individuals that you are interested in studying, while the sampling method is the process by which you will select individuals from the population to participate in your study.

By formulating your research design before beginning your study, you can ensure that your data will be collected and analyzed effectively. This will ultimately help you to answer your research question and draw conclusions about your topic of interest. So, take some time to consider all of these factors before moving on to the next step in your research journey!

5. Select the research method

Once the research design has been selected, the next step is to select the research method. This will determine how data will be collected and can include methods such as interviews, focus groups, and surveys.

The research method should be selected based on the research design and the research question. As mentioned, some of the most common research methods are interviews, focus groups, and surveys. Each research method has its own advantages and disadvantages. For example, interviews are good for getting in-depth information from a small number of people, but they can be time-consuming and expensive. Focus groups are good for exploring ideas with a group of people, but they can be difficult to control. Surveys are good for collecting large amounts of data quickly, but they can be subject to bias.

Once the research method has been selected, the next step is to develop the research instruments . These will be used to collect data from participants in the study. The most common research instruments are questionnaires and interview protocols.

Questionnaires are a type of research instrument that is used to collect data from participants in a study. They can be used to collect both quantitative and qualitative data. Questionnaires can be administered in person, by mail, or online.

Interview protocols are another type of research instrument that is used to collect data from participants in a study. They are typically used to collect qualitative data. Interview protocols can be administered in person or by telephone.

6. Collect data

After selecting the research method, it is time to start collecting data. This can be done through a number of different methods, depending on the type of study and research objectives.

There are a few things to keep in mind when collecting data. First, you need to decide what type of data you need. Second, you need to choose the right methods for Collecting that data. And third, you need to make sure that the data you collect is high quality. let’s take a closer look at each of these points.

When deciding what type of data you need, it is important to consider what type of research questions you are trying to answer. If your research questions are qualitative in nature, then you will likely want to collect qualitative data. Qualitative data includes things like interviews, focus groups, and observations. If your research questions are quantitative in nature, then you will want to Collect quantitative data. Quantitative data includes things like surveys, experiments, and demographic information.

Once you have decided what type of data you need, you need to choose the right Collecting methods. There are many different Collecting methods, and the right method will depend on the type of data you are Collecting and your research goals. Some common Collecting methods include interviews, focus groups, online surveys, experiments, and observations.

When Collecting data, it is important to make sure that the data is high quality. This means that the data should be accurate, reliable, and valid. Data quality is important because it affects the validity of your research findings. If your data is not high quality, then your research findings might not be accurate. Collecting high quality data takes time and effort, but it is worth it to make sure that your research findings are accurate.

7. Clean and code data

After data has been collected, it must be cleaned and coded. This process helps to ensure that the data is ready for analysis. There are a few things to keep in mind when collecting data. 

• First, make sure that the data is accurate and reliable. This means choosing a method that will produce valid results. 
• Second, the data should be representative of the population being studied. 
• Third, collect enough data to answer the research question(s).

There are a few different ways to collect data. Some common methods include surveys, interviews, focus groups, and observations. Collecting data can be a time-consuming process, so it is important to plan ahead and allow enough time to gather all the necessary information. Once the data has been collected, it is time to analyze it. This will be covered in the next section.

8. Analyze data

Once the data has been cleaned and coded, it is time to begin analyzing it. This can be done through a number of different methods, such as descriptive statistics, t-tests, and regression analysis.

The first step in analysis is to decide what type of analysis is best suited for the research question. Descriptive statistics can be used to summarize the data and give an overall picture of what is going on. T-tests can be used to compare means between two groups, and regression analysis can be used to examine the relationships between variables.

You can use tools like IMB SPSS Software to perform all sorts of statical tests and that way “bridge the gap between data science and data understanding”. We’ve found the bellow “SPSS Tutorial for data analysis | SPSS for Beginners” tutorial video quite useful and comprehensive. 

Once the appropriate analyses have been selected, they need to be conducted. This involves running the analyses and interpreting the results. Results should be reported in a clear and concise manner, with enough detail that someone else could replicate the analyses if they wanted to.

After the data has been analyzed, it is time to write up the results. This usually takes the form of a research paper or report. The results should be presented in a way that is easy to understand, and the implications of the findings should be discussed.

This is just a brief overview of data analysis; there are many resources available that can provide more detailed information. The important thing is to get started and to keep learning as you go. With practice, analyzing data will become easier and more enjoyable.

9. Interpret data and test hypotheses

After the data has been analyzed, it is important to interpret it. This includes understanding the results of the study and what they mean for the research problem or opportunity.

When interpreting data, it is important to consider the following:

• The results of the study and what they mean for the research problem or opportunity
• The reliability and validity of the data
• The limitations of the study
• The implications of the findings

Once the data has been interpreted, it is then time to test hypotheses. This involves using statistical techniques to test whether there is a significant relationship between two or more variables.

Testing hypotheses is an important part of any scientific research as it allows researchers to determine whether their results are statistically significant. If a hypothesis is found to be statistically significant, it means that there is a real relationship between the variables being tested. If a hypothesis is not statistically significant, it means that there is no real relationship between the variables being tested.

When testing hypotheses, it is important to consider the following:

• The null hypothesis
• The alternative hypothesis
• The level of significance
• The statistical test used

Once the hypotheses have been tested, it is then time to draw conclusions. This involves Interpret data and test hypotheses reviewing the findings of the study and determining what they mean for the research problem or opportunity. When drawing conclusions, it is important to consider the following:

• The implications of the findings.

Interpret data and test hypotheses are two important steps in scientific research process. By understanding and applying these steps, researchers can ensure that their findings are accurate and reliable.

10. Write the report

After analyzing and interpreting the data, it is time to write the report. This should include a detailed description of the research process, findings, and conclusions of the study.

The research report should be written in a clear, concise, and easy-to-understand manner. It should be free of jargon and technical language, and should be accessible to a wide audience. The report should also be well-organized and well- structured.

When writing the research report, it is important to keep in mind the purpose of the research. The research report should answer the research question(s), and should address the objectives of the study. The findings of the research should be presented in a logical and coherent manner.

The conclusion of the research report should summarize the findings of the study, and should discuss their implications. The recommendations of the study should also be included in the conclusion section.

11. Present the findings

The final step is to present the findings of the study. This can be done through a number of different methods, such as presentations, posters, and reports.

The findings of the research should be presented in a way that is clear and concise. The presentation should be designed to engage the audience and encourage them to ask questions. The findings should be tailored to the specific audience, taking into account their background knowledge and understanding.

One method of presenting research findings is through a poster. Posters are a great way to summarise complex information and allow people to take away key points. They can also be used as a starting point for discussions. Another option is to give a presentation, which can be done either in person or online. Presentations offer the opportunity to go into more detail than a poster, and they can also be recorded so that they can be shared with people who were not able to attend.

Whatever method is used, it is important to remember that the research findings should be the focus of the presentation. The aim is to communicate the findings clearly and effectively, not to simply show off the work that has been done. With this in mind, it is often best to keep things simple and avoid using jargon or complex terminology.

Things to consider when presenting research findings

• Keep the audience in mind
• Present findings in a clear and concise manner
• Engage the audience and encourage questions
• Use simple language and avoid jargon whenever possible. Try explaining concepts in everyday terms.
• Focus on the research findings themselves, not on other aspects of the project.

Remember that the goal is to communicate the findings effectively.

There are a number of different ways to present research findings. Some common methods include:

• Presentations (in person or online)

Choose the method that best suits the audience and the message you want to communicate. And don’t forget – keep it simple!

Try explaining concepts in everyday terms. This will make it easier for your audience to understand your research findings.

Another important tip is to focus on the research findings themselves, not on other aspects of the project. The goal is to communicate the findings effectively, so avoid getting sidetracked by other details.

When presenting research findings, it is also important to use simple language and avoid jargon whenever possible. Try explaining concepts in everyday terms. This will make it easier for your audience to understand your research findings.

Remember that the goal is to communicate the findings effectively. With this in mind, it is often best to keep things simple and avoid using jargon or complex terminology.

One final tip: focus on the research findings themselves, not on other aspects of the project. The aim is to communicate the findings clearly and effectively, not to simply show off the work that has been done.

Keep these tips in mind when presenting research findings, and you’ll be sure to engage and inform your audience. 

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Research Process – Steps, Examples and Tips

Table of Contents

Research Process

Definition:

Research Process is a systematic and structured approach that involves the collection, analysis, and interpretation of data or information to answer a specific research question or solve a particular problem.

Research Process Steps

Research Process Steps are as follows:

Identify the Research Question or Problem

This is the first step in the research process. It involves identifying a problem or question that needs to be addressed. The research question should be specific, relevant, and focused on a particular area of interest.

Conduct a Literature Review

Once the research question has been identified, the next step is to conduct a literature review. This involves reviewing existing research and literature on the topic to identify any gaps in knowledge or areas where further research is needed. A literature review helps to provide a theoretical framework for the research and also ensures that the research is not duplicating previous work.

Formulate a Hypothesis or Research Objectives

Based on the research question and literature review, the researcher can formulate a hypothesis or research objectives. A hypothesis is a statement that can be tested to determine its validity, while research objectives are specific goals that the researcher aims to achieve through the research.

Design a Research Plan and Methodology

This step involves designing a research plan and methodology that will enable the researcher to collect and analyze data to test the hypothesis or achieve the research objectives. The research plan should include details on the sample size, data collection methods, and data analysis techniques that will be used.

Collect and Analyze Data

This step involves collecting and analyzing data according to the research plan and methodology. Data can be collected through various methods, including surveys, interviews, observations, or experiments. The data analysis process involves cleaning and organizing the data, applying statistical and analytical techniques to the data, and interpreting the results.

Interpret the Findings and Draw Conclusions

After analyzing the data, the researcher must interpret the findings and draw conclusions. This involves assessing the validity and reliability of the results and determining whether the hypothesis was supported or not. The researcher must also consider any limitations of the research and discuss the implications of the findings.

Communicate the Results

Finally, the researcher must communicate the results of the research through a research report, presentation, or publication. The research report should provide a detailed account of the research process, including the research question, literature review, research methodology, data analysis, findings, and conclusions. The report should also include recommendations for further research in the area.

Review and Revise

The research process is an iterative one, and it is important to review and revise the research plan and methodology as necessary. Researchers should assess the quality of their data and methods, reflect on their findings, and consider areas for improvement.

Ethical Considerations

Throughout the research process, ethical considerations must be taken into account. This includes ensuring that the research design protects the welfare of research participants, obtaining informed consent, maintaining confidentiality and privacy, and avoiding any potential harm to participants or their communities.

Dissemination and Application

The final step in the research process is to disseminate the findings and apply the research to real-world settings. Researchers can share their findings through academic publications, presentations at conferences, or media coverage. The research can be used to inform policy decisions, develop interventions, or improve practice in the relevant field.

Research Process Example

Following is a Research Process Example:

Research Question : What are the effects of a plant-based diet on athletic performance in high school athletes?

Step 1: Background Research Conduct a literature review to gain a better understanding of the existing research on the topic. Read academic articles and research studies related to plant-based diets, athletic performance, and high school athletes.

Step 2: Develop a Hypothesis Based on the literature review, develop a hypothesis that a plant-based diet positively affects athletic performance in high school athletes.

Step 3: Design the Study Design a study to test the hypothesis. Decide on the study population, sample size, and research methods. For this study, you could use a survey to collect data on dietary habits and athletic performance from a sample of high school athletes who follow a plant-based diet and a sample of high school athletes who do not follow a plant-based diet.

Step 4: Collect Data Distribute the survey to the selected sample and collect data on dietary habits and athletic performance.

Step 5: Analyze Data Use statistical analysis to compare the data from the two samples and determine if there is a significant difference in athletic performance between those who follow a plant-based diet and those who do not.

Step 6 : Interpret Results Interpret the results of the analysis in the context of the research question and hypothesis. Discuss any limitations or potential biases in the study design.

Step 7: Draw Conclusions Based on the results, draw conclusions about whether a plant-based diet has a significant effect on athletic performance in high school athletes. If the hypothesis is supported by the data, discuss potential implications and future research directions.

Step 8: Communicate Findings Communicate the findings of the study in a clear and concise manner. Use appropriate language, visuals, and formats to ensure that the findings are understood and valued.

Applications of Research Process

The research process has numerous applications across a wide range of fields and industries. Some examples of applications of the research process include:

• Scientific research: The research process is widely used in scientific research to investigate phenomena in the natural world and develop new theories or technologies. This includes fields such as biology, chemistry, physics, and environmental science.
• Social sciences : The research process is commonly used in social sciences to study human behavior, social structures, and institutions. This includes fields such as sociology, psychology, anthropology, and economics.
• Education: The research process is used in education to study learning processes, curriculum design, and teaching methodologies. This includes research on student achievement, teacher effectiveness, and educational policy.
• Healthcare: The research process is used in healthcare to investigate medical conditions, develop new treatments, and evaluate healthcare interventions. This includes fields such as medicine, nursing, and public health.
• Business and industry : The research process is used in business and industry to study consumer behavior, market trends, and develop new products or services. This includes market research, product development, and customer satisfaction research.
• Government and policy : The research process is used in government and policy to evaluate the effectiveness of policies and programs, and to inform policy decisions. This includes research on social welfare, crime prevention, and environmental policy.

Purpose of Research Process

The purpose of the research process is to systematically and scientifically investigate a problem or question in order to generate new knowledge or solve a problem. The research process enables researchers to:

• Identify gaps in existing knowledge: By conducting a thorough literature review, researchers can identify gaps in existing knowledge and develop research questions that address these gaps.
• Collect and analyze data : The research process provides a structured approach to collecting and analyzing data. Researchers can use a variety of research methods, including surveys, experiments, and interviews, to collect data that is valid and reliable.
• Test hypotheses : The research process allows researchers to test hypotheses and make evidence-based conclusions. Through the systematic analysis of data, researchers can draw conclusions about the relationships between variables and develop new theories or models.
• Solve problems: The research process can be used to solve practical problems and improve real-world outcomes. For example, researchers can develop interventions to address health or social problems, evaluate the effectiveness of policies or programs, and improve organizational processes.
• Generate new knowledge : The research process is a key way to generate new knowledge and advance understanding in a given field. By conducting rigorous and well-designed research, researchers can make significant contributions to their field and help to shape future research.

Tips for Research Process

Here are some tips for the research process:

• Start with a clear research question : A well-defined research question is the foundation of a successful research project. It should be specific, relevant, and achievable within the given time frame and resources.
• Conduct a thorough literature review: A comprehensive literature review will help you to identify gaps in existing knowledge, build on previous research, and avoid duplication. It will also provide a theoretical framework for your research.
• Choose appropriate research methods: Select research methods that are appropriate for your research question, objectives, and sample size. Ensure that your methods are valid, reliable, and ethical.
• Be organized and systematic: Keep detailed notes throughout the research process, including your research plan, methodology, data collection, and analysis. This will help you to stay organized and ensure that you don’t miss any important details.
• Analyze data rigorously: Use appropriate statistical and analytical techniques to analyze your data. Ensure that your analysis is valid, reliable, and transparent.
• I nterpret results carefully : Interpret your results in the context of your research question and objectives. Consider any limitations or potential biases in your research design, and be cautious in drawing conclusions.
• Communicate effectively: Communicate your research findings clearly and effectively to your target audience. Use appropriate language, visuals, and formats to ensure that your findings are understood and valued.
• Collaborate and seek feedback : Collaborate with other researchers, experts, or stakeholders in your field. Seek feedback on your research design, methods, and findings to ensure that they are relevant, meaningful, and impactful.

About the author

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Research Design and Process

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Research design aims to provide a rationale, framework and structure before engaging with data collection and data analysis (Vaus, Research design in social research, Sage, 2001). A reasonable research design defines the structure of the research process, arrangement of the different methods required to respond to the research questions and the different outputs at each of the stages established.

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Chen, H., Baptista Nunes, M. (2023). Research Design and Process. In: Professional Empowerment in the Software Industry through Experience-Driven Shared Tacit Knowledge. Springer, Singapore. https://doi.org/10.1007/978-981-99-1486-9_4

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• Indian J Crit Care Med
• v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.

CLASSIFICATION OF STUDY DESIGNS

Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

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Conflict of interest: None

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Research Process Steps: What they are + How To Follow

There are various approaches to conducting basic and applied research. This article explains the research process steps you should know. Whether you are doing basic research or applied research, there are many ways of doing it. In some ways, each research study is unique since it is conducted at a different time and place.

Conducting research might be difficult, but there are clear processes to follow. The research process starts with a broad idea for a topic. This article will assist you through the research process steps, helping you focus and develop your topic.

Research Process Steps

The research process consists of a series of systematic procedures that a researcher must go through in order to generate knowledge that will be considered valuable by the project and focus on the relevant topic.

To conduct effective research, you must understand the research process steps and follow them. Here are a few steps in the research process to make it easier for you:

Step 1: Identify the Problem

Finding an issue or formulating a research question is the first step. A well-defined research problem will guide the researcher through all stages of the research process, from setting objectives to choosing a technique. There are a number of approaches to get insight into a topic and gain a better understanding of it. Such as:

• A preliminary survey
• Case studies
• Interviews with a small group of people
• Observational survey

Step 2: Evaluate the Literature

A thorough examination of the relevant studies is essential to the research process . It enables the researcher to identify the precise aspects of the problem. Once a problem has been found, the investigator or researcher needs to find out more about it.

This stage gives problem-zone background. It teaches the investigator about previous research, how they were conducted, and its conclusions. The researcher can build consistency between his work and others through a literature review. Such a review exposes the researcher to a more significant body of knowledge and helps him follow the research process efficiently.

Step 3: Create Hypotheses

Formulating an original hypothesis is the next logical step after narrowing down the research topic and defining it. A belief solves logical relationships between variables. In order to establish a hypothesis, a researcher must have a certain amount of expertise in the field. 

It is important for researchers to keep in mind while formulating a hypothesis that it must be based on the research topic. Researchers are able to concentrate their efforts and stay committed to their objectives when they develop theories to guide their work.

Step 4: The Research Design

Research design is the plan for achieving objectives and answering research questions. It outlines how to get the relevant information. Its goal is to design research to test hypotheses, address the research questions, and provide decision-making insights.

The research design aims to minimize the time, money, and effort required to acquire meaningful evidence. This plan fits into four categories:

• Exploration and Surveys
• Data Analysis
• Observation

Step 5: Describe Population

Research projects usually look at a specific group of people, facilities, or how technology is used in the business. In research, the term population refers to this study group. The research topic and purpose help determine the study group.

Suppose a researcher wishes to investigate a certain group of people in the community. In that case, the research could target a specific age group, males or females, a geographic location, or an ethnic group. A final step in a study’s design is to specify its sample or population so that the results may be generalized.

Step 6: Data Collection

Data collection is important in obtaining the knowledge or information required to answer the research issue. Every research collected data, either from the literature or the people being studied. Data must be collected from the two categories of researchers. These sources may provide primary data.

• Questionnaire

Secondary data categories are:

• Literature survey
• Official, unofficial reports
• An approach based on library resources

Step 7: Data Analysis

During research design, the researcher plans data analysis. After collecting data, the researcher analyzes it. The data is examined based on the approach in this step. The research findings are reviewed and reported.

Data analysis involves a number of closely related stages, such as setting up categories, applying these categories to raw data through coding and tabulation, and then drawing statistical conclusions. The researcher can examine the acquired data using a variety of statistical methods.

Step 8: The Report-writing

After completing these steps, the researcher must prepare a report detailing his findings. The report must be carefully composed with the following in mind:

• The Layout: On the first page, the title, date, acknowledgments, and preface should be on the report. A table of contents should be followed by a list of tables, graphs, and charts if any.
• Introduction: It should state the research’s purpose and methods. This section should include the study’s scope and limits.
• Summary of Findings: A non-technical summary of findings and recommendations will follow the introduction. The findings should be summarized if they’re lengthy.
• Principal Report: The main body of the report should make sense and be broken up into sections that are easy to understand.
• Conclusion: The researcher should restate his findings at the end of the main text. It’s the final result.

LEARN ABOUT: 12 Best Tools for Researchers

The research process involves several steps that make it easy to complete the research successfully. The steps in the research process described above depend on each other, and the order must be kept. So, if we want to do a research project, we should follow the research process steps.

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What is Research Design? Characteristics, Types, Process, & Examples

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Your search has come to an end!

Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? Think of it as your roadmap, ensuring you don't end up lost in a sea of confusing data. So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design in research? Well, research design is the plan that shows how the study will be done. This plan covers everything from how data will be collected to how it will be analysed. A good research design has a clear question to answer, a detailed plan for gathering information, and a way to make sense of the findings. A good research design has three key ingredients:

1. A clear question: What exactly are you trying to learn? ‍

2. Data collection: How will you gather information (surveys, interviews, experiments)?

3. Analysis: How will you make sense of the data you collect?

Elements of Research Design 

Now that you know what is research design, it is important to know the elements. The elements or components of research design help to ensure that it is reliable, valid and can yield meaningful results. They also provide a guide for the research process, helping the researcher from the initial stages of formulating the research question to the final stages of interpreting the findings. 

1. Purpose Statement: This is a clear and concise statement of the research objectives and the specific goals the research aims to achieve.

2. Research Questions: These are the specific questions the research aims to answer.

3. Research Methodology: This refers to the overall approach and specific methods used to collect and analyse data.

4. Data Collection Methods: These are the specific techniques used to gather data for the research.

5. Data Analysis Techniques: These are the methods used to analyse and interpret the collected data.

6. Units of Analysis: These are the specific entities (e.g., individuals, groups, organisations) that the research focuses on.

7. Linking Data to Propositions: This involves connecting the data collected to the research questions or hypotheses.

8. Interpretation of Findings: This involves making sense of the data and drawing conclusions based on the research objectives.

9. Possible Obstacles to the Research: This involves identifying potential challenges or issues that may arise during the research process.

10. Settings for Research Study: This refers to the context or environment in which the research is conducted.

11. Time of the Research Study: This refers to the timeframe of the research, whether it’s cross-sectional (at one specific point in time) or longitudinal (over an extended period).

Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability: A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity: A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality: A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability: A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

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The Process of Research Design

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

Step 1: Establish Priorities for Research Design: 

Before conducting any research study, you must address an important question: "what is research design and how to create one?" For example, if you're researching the impact of remote learning on student performance, your priority might be to establish a clear research question and objectives.

Step 2: Choose your Data Type you Need for Research

One of the best features of research design is to decide on the type of data you need for your research. For instance, if you’re studying the effects of a new drug, you might need quantitative data like clinical trial results.

There are lots of ways to answer your research questions. Think about what you want to achieve before you decide how to do your research. The first thing, do you know what is qualitative research design and what is quantitative research design? Here's a quick difference between the two:

What is Research Design in Quantitative Research?

There are 4 main types of quantitative research design- 

What are Research Design Examples?

1. Experimental Research Methods: 

Drug Efficacy Study: A pharmaceutical company wants to test the effectiveness of a new drug. They randomly assign participants to two groups: one group receives the new drug (experimental group), and the other group receives a placebo (control group). The company then measures the health outcomes of the two groups.

2. Quasi-Experimental Research Methods:

Teaching Method Evaluation: A researcher is interested in the impact of a new teaching method. A group of students are taught using the new method, while another group is taught using the traditional method. The researcher then compares the academic performance of the two groups.

3. Descriptive Research Methods:

Consumer Behavior Survey: A company wants to understand the shopping habits of their customers. They conduct a survey asking customers about their shopping frequency, preferred products, and reasons for their preferences.

4. Correlational Research Methods:

Health and Lifestyle Study: A health researcher is interested in the relationship between physical activity levels and heart disease. They collect data on the physical activity levels and heart health of a large group of people over several years. The researcher then analyses the data to see if there is a correlation between physical activity and heart disease

What is Qualitative Research Design?

Qualitative research designs are more flexible and open-ended. They're all about deeply understanding a particular situation or topic, and you have room to be imaginative and adaptable in planning your study. Below, you'll find a list of typical qualitative research designs.

Step 3: Decide your Data Collection Techniques

Now that you understand what is research design in research, you should also know the types of what are the different types of research design techniques. Choose the methods you’ll use to gather your data. If you’re surveying consumer behaviour, for example, you might use questionnaires or interviews.

Survey methods

Surveys are like questionnaires or interviews where you ask people about what they think, do, feel, or are like. They help you gather information straight from the source. So, when you're planning a research project, you can pick either questionnaires or interviews as your main way to get data. Research design is just the plan you make for how you're going to do your research, including what methods you'll use, like surveys.

Observation methods

Observational studies are a way to gather information without bothering anyone. You just watch and note down what you see, like people's actions or how they interact, without asking them directly. You can do this right then and there, jotting down stuff, or you can record videos to check out later. Depending on what you're studying, these observations can focus on describing things or counting them up.

Secondary Data

If you can't gather data yourself, you can use info already collected by other researchers, like from government surveys or past studies. You can then analyse this data to explore new questions. This can broaden your research because you might access bigger and more diverse samples. But, since you didn't collect the data yourself, you can't choose what to measure or how, which limits your conclusions.

In simple terms, research design is about how you plan to gather and analyse data to answer your research questions. If you can't collect data directly, you might use data already gathered by others, known as secondary data, to still answer your questions.

Step 4: Sort Out your Data Analysis

When you find what research design in research, just having a bunch of raw data isn't enough to answer your questions. You also need to figure out how you're going to make sense of that data. This is where research design comes in.

If you're working with quantitative research, you'll probably use statistics to analyse your data. Statistics help you understand things like how your data is spread out, what the average is, and how different groups compare. For example, you might use tests to see if there's a connection between two things or if one group is different from another.

But if you're dealing with more qualitative research, you'll need a different approach. Instead of crunching numbers, you'll be diving deep into your data, looking for patterns and meanings. You might use methods like thematic analysis or discourse analysis to make sense of it all.

Sampling Procedures

Choosing the right way to pick people for your study is important. But it's not just about that. You also need a solid plan for how you'll reach out and get those people to join in.

Here's what you need to think about:

1. How many people do you need to join to make sure your study is good?

2. What rules will you use to decide who can join and who can't?

3. How will you get in touch with them—by mail, online, phone, or meeting them in person?

4. If you're picking people randomly, it's crucial that everyone who gets chosen actually takes part. How can you make sure most of them do?

If you're not picking people randomly, how will you ensure that your study is unbiased and represents different kinds of people? 

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the 4 types of research design, what are the important concepts of research design, what are the 5 components of a research, what are different types of research, what are the 4 major elements of a research design.

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Types of Research Design: Process and Elements

• Updated on  
• Nov 25, 2023

Types of Research Design : Be it science and technology , art and culture, media studies, geography , mathematics , and other subjects, research has always been the route towards finding the unknown. In the circumstances when Coronavirus shattered the world, a vast amount of research was being carried out to find vaccines for its treatment. In this blog, we will understand what are the various types of research design and their related components. 

This Blog Includes:

Descriptive research design, experimental research design, correlational research design, diagnostic research design, explanatory research design, process of research design, what is research design, elements of research design, quantitative research design, qualitative research design, quantitative vs. qualitative research design, fixed vs. flexible research design, how to write research design, cohort study, cross-sectional study, longitudinal study, cross-sequential study, types of research design pdf, research design ppt, benefits of research.

Also Read: Research Institutes in India

Types of Research Designs

Now that we know the broadly classified types of research, Quantitative and Qualitative Research can be divided into the following 4 major types of Research Designs:

✏️ Descriptive Research Design ✏️ Case Study ✏️ Correlational Research Design ✏️ Experimental Research Design ✏️ Diagnostic Research Design ✏️ Explanatory Research Design ✏️ Historical research design ✏️ Cohort research design ✏️ Sequential Research Design ✏️ Action Research Design ✏️ Survey

✏️ Phone System ✏️ Causal Research Design

These types of Research Designs mentioned below are considered the closest and exact to true experiments and are preferred in terms of accuracy, relevance as well as quality.

In Descriptive Research Design, the scholar explains/describes the situation or case in depth in their research materials. This type of research design is purely on a theoretical basis where the individual collects data, analyses, prepares and then presents it in an understandable manner. It is the most generalised form of research design. To explore one or more variables, a descriptive design might employ a wide range of research approaches. Unlike in experimental research, the researcher does not control or change any of the variables in a descriptive research design; instead, he or she just observes and measures them.  In other words, while qualitative research may also be utilised for descriptive reasons, a descriptive method of research design is typically regarded as a sort of quantitative research. To guarantee that the results are legitimate and dependable, the study design should be properly constructed. Here are some examples of the descriptive design of the research type:

• How has the Delhi housing market changed over the past 20 years?
• Do customers of Company A prefer Product C or Product D?
• What are the main genetic, behavioural and morphological differences between Indian wild cows and hybrid cows?
• How prevalent is disease 1 in population Z?

Experimental research is a type of research design in which the study is carried out utilising a scientific approach and two sets of variables. The first set serves as a constant against which the variations in the second set are measured. Experimentation is used in quantitative research methodologies, for example. If you lack sufficient evidence to back your conclusions, you must first establish the facts. Experimental research collects data to assist you in making better judgments. Experimentation is used in any research undertaken in scientifically appropriate settings. The effectiveness of experimental investigations is dependent on researchers verifying that a variable change is due only to modification of the constant variable. The study should identify a noticeable cause and effect. The traditional definition of experimental design is “the strategies employed to collect data in experimental investigations.” There are three types of experimental designs:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

A correlational research design looks into correlations between variables without allowing the researcher to control or manipulate any of them. Correlational studies reveal the magnitude and/or direction of a link between two (or more) variables. Correlational studies or correlational study designs might have either a positive, negative or zero.

Correlational research design is great for swiftly collecting data from natural settings. This allows you to apply your results to real-world circumstances in an externally legitimate manner. Correlational studies research is a viable choice in a few scenarios like:

• To investigate non-causal relationships
• To explore causal relationships between variables
• To test new measurement tools

Recommended Read: Scope of Operation Research

Diagnostic research design is a type of research design that tries to investigate the underlying cause of a certain condition or phenomenon. It can assist you in learning more about the elements that contribute to certain difficulties or challenges that your clients may be experiencing. This design typically consists of three research stages, which are as follows:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

Explanatory research is a method established to explore phenomena that have not before been researched or adequately explained. Its primary goal is to notify us about where we may get a modest bit of information. With this strategy, the researcher obtains a broad notion and uses research as a tool to direct them more quickly to concerns that may be addressed in the future. Its purpose is to discover the why and what of a subject under investigation. In short, it is a type of research design that is responsible for finding the  why  of the events through the establishment of cause-effect relationships. The most popular methods of explanatory research are:

• Literature research
• In-depth interview
• Focus groups
• Case studies

Is it possible to conduct research without a plan? Most likely not. Research design is a topic we cover while discussing a plan for gathering, analyzing, and interpreting data. This design solves issues and produces a coherent and consistent data analysis model. Let’s study up on it.

A methodical and planned technique for conducting research is the research design process. To make sure the study is legitimate, trustworthy, and yields insightful data, the procedure is crucial. One should keep the points in mind while preparing for research.

✅ Think about your goals and strategies : Establish the study’s theoretical framework, methods, and research questions and objectives. ✅ Select a kind of study design : Based on the research questions and objectives, choose the best research design, such as experimental, correlational, survey, case study, or ethnographic. ✅ Decide on your sample technique and population : Establish the sample size and target population before selecting a sampling strategy, such as convenience, stratified, or random sampling. ✅ Select the techniques you’ll use to collect data : Choose the right instruments or tools for data collection and decide on the methodologies, such as surveys, interviews, observations, or experiments. ✅ Arrange the steps you’ll take to collect data : Create a plan for gathering data that takes ethics into account and specifies the time, place, and people involved. ✅ Choose your data analysis techniques : Plan how to interpret the findings after choosing the relevant data analysis methods, such as statistical, content, or discourse analysis.

Also Read: 10 Types of Qualitative Research Methods & Examples

By the term ‘ research ‘, we can understand that it’s a collection of data that includes critical information by taking research methodologies into consideration. In other words, it is a compilation of information or data explored by setting a hypothesis and consequently coming up with substantive findings in an organised way. Research can be done on an academic as well as a scientific basis as well. Let’s first understand what a research design actually means.

A Research Design is simply a structural framework of various research methods as well as techniques that are utilised by a researcher.

The research design helps a researcher to pursue their journey into the unknown but with a systematic approach by their side. The way an engineer or architect frames a design for a structure, likewise the researcher picks the design from various approaches in order to check which type of research to be carried out. 

Here are the most important elements of a research design- 

➡️ The method applied for analyzing collected details ➡️ Type of research methodology ➡️ Accurate purpose statement ➡️ Probable objections to research ➡️ Techniques to be implemented for collecting and analyzing research ➡️ Timeline ➡️ Measurement of analysis ➡️ Settings for the research study

Must Read: What does a Research Assistant do?

Get to know about the characteristics of Research Design through the infographic given below.

2 Major Types of Research Design 

Keeping its dynamics into consideration, the research design is categorised into two different perspectives, i.e. Quantitative Research Design and Qualitative Research Design . Further, there are four main characteristics of research design which include Reliability, Neutrality, Validity as well as Generalization. Further, a researcher should have a clear understanding of how their project can be implemented in the research design. Let’s explore what Quantitative and Qualitative Research Designs mean:

In Quantitative Research Design, a researcher examines the various variables while including numbers as well as statistics in a project to analyze its findings. The use of graphics, figures, and pie charts is the main form of data collection measurement and meta-analysis (it is information about the data by the data).

This type of research is quite contrary to the quantitative research design. It is explanatory in nature and always seeks answers to “What’s” and “How’s”. It mainly focuses on why a specific theory exists and what would be the respondent’s answer to it. This allows a researcher to draw a conclusion with proper findings. Case studies are mainly used in Qualitative Research Design in order to understand various social complexities. 

Know All About Business Research!

Following is the difference between Quantitative vs. Qualitative Research Design

A contrast between fixed and flexible research design can also be drawn. Quantitative (fixed design) and qualitative (flexible design) data gathering are frequently associated with these two study design categories. The research design is pre-determined and understood with a set study design even before you begin collecting data. Flexible designs, on the other hand, provide for more flexibility in data collection — for example, you don’t provide fixed answer alternatives, so respondents must put in their own responses.

Let’s learn how to create and write a research design!

Research Design Types by Grouping

Another classification of study design types is based on how participants are categorized. In most situations, grouping is determined by the research premise and the method used to sample individuals. There is generally at least one experimental and one control group in a typical study based on experimental research design.

In medical research, for example, one group can be given therapy while the other receives none. You get my drift. We can differentiate four types of study designs based on participant grouping:

A cohort study is a sort of longitudinal research that takes a cross-section of a cohort (a group of people who have a common trait) at predetermined time intervals. It’s a form of panel research in which all of the people in the group have something in common.

In social science, medical research, and biology, a cross-sectional study is prevalent. This study approach examines data from a population or a representative sample of the population at a specific point in time.

A longitudinal study is a type of study in which the same variables are observed repeatedly over a short or long period of time. It’s usually observational research, although it can also take the form of a long-term randomized experiment.

Cross-sequential research design combines longitudinal and cross-sectional research methods, with the goal of compensating for some of the flaws inherent in both.

Since we are dealing with the types of research design, it is imperative to understand how beneficial the practice of doing research is and some of its major advantages are:

• Research helps in getting a deeper understanding of the subject.
• You will learn about its varied aspects as well as its different sources like primary and secondary.
• It helps to resolve complex problems in any field through critical analysis and measurement of unsolved problems. 
• You will also get to know how a hypothesis is created by weighing preserved assumptions.

Also Read: How to Make a Career in Research?  

Research designs can be classified into four main categories: descriptive, correlational, experimental, and diagnostic designs.

The five primary types of study design approaches utilized in research disciplines are explanatory, diagnostic, correlational, experimental, and descriptive research.

Quasi-experimental design is a research design in which the researcher does not have complete control over the independent variable, and therefore cannot establish a cause-and-effect relationship. However, they can still examine the relationship between variables.

Correlational design is a research design in which the researcher examines the relationship between two or more variables, without manipulating any of them.

Certainly, research is the fuel that can potentially drive the solutions to redress all the world’s problems. In order to help to gain a deeper understanding of any subject matter, knowing types of research design plays a critical role in carrying out your thesis. If you are aspiring to pursue your career in the field of research and aim to pursue a PhD , call us at 1800572000 for a free 30-minute career counselling session with our Leverage Edu experts and we will help you find a suitable program and university that fit your aspirations, interests and preferences and can guide you towards a fulfilling career in this domain.

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12 comments

I was able to made very good understanding on research design types

Hey Maurice!

We are really glad to hear that. Do subscribe to our newsletter to get the latest updates! Thank you.

CLEAR AND HELPFULL 😍

would like more lessons

Thanks you have being of help to me janees

absolutely good notes thanks

I need for thesis work

Hi Jiregna,

We have a few blogs on thesis work that may help you further- https://leverageedu.com/blog/phd-thesis/ https://leverageedu.com/blog/dphil/

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This stage is about gathering initial insights to set a clear direction.

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• Open access
• Published: 17 May 2024

Co-designing Entrustable Professional Activities in General Practitioner’s training: a participatory research study

• Vasiliki Andreou   ORCID: orcid.org/0000-0002-6679-0514 1 , 4 ,
• Sanne Peters   ORCID: orcid.org/0000-0001-6235-1752 1 , 2 ,
• Jan Eggermont   ORCID: orcid.org/0000-0002-8497-1159 3 &
• Birgitte Schoenmakers   ORCID: orcid.org/0000-0003-1909-9613 1  

BMC Medical Education volume  24 , Article number:  549 ( 2024 ) Cite this article

36 Accesses

Metrics details

In medical education, Entrustable Professional Activities (EPAs) have been gaining momentum for the last decade. Such novel educational interventions necessitate accommodating competing needs, those of curriculum designers, and those of users in practice, in order to be successfully implemented.

We employed a participatory research design, engaging diverse stakeholders in designing an EPA framework. This iterative approach allowed for continuous refinement, shaping a comprehensive blueprint comprising 60 EPAs. Our approach involved two iterative cycles. In the first cycle, we utilized a modified-Delphi methodology with clinical competence committee (CCC) members, asking them whether each EPA should be included. In the second cycle, we used semi-structured interviews with General Practitioner (GP) trainers and trainees to explore their perceptions about the framework and refine it accordingly.

During the first cycle, 14 CCC members agreed that all the 60 EPAs should be included in the framework. Regarding the formulation of each EPAs, 20 comments were given and 16 adaptations were made to enhance clarity. In the second cycle, the semi-structured interviews with trainers and trainees echoed the same findings, emphasizing the need of the EPA framework for improving workplace-based assessment, and its relevance to real-world clinical scenarios. However, trainees and trainers expressed concerns regarding implementation challenges, such as the large number of EPAs to be assessed, and perception of EPAs as potentially high-stakes.

Accommodating competing stakeholders’ needs during the design process can significantly enhance the EPA implementation. Recognizing users as experts in their own experiences empowers them, enabling a priori identification of implementation barriers and potential pitfalls. By embracing a collaborative approach, wherein diverse stakeholders contribute their unique viewpoints, we can only create effective educational interventions to complex assessment challenges.

Peer Review reports

Introduction

In recent years, the landscape of medical education has significantly transformed due to increasing demands of public accountability and changing patient needs. In response to these evolving demands, competency-based medical education (CBME) has emerged. CBME has been gaining popularity in medical education programs [ 1 ]. In a CBME paradigm, medical curricula are structured based on predefined competencies that physicians should have acquired upon completion of the program [ 2 , 3 ]. Despite the theoretical underpinnings of CBME, its implementation has encountered various obstacles [ 4 ]. Particularly, assessing competencies in real clinical environments has been a major barrier in the effective integration of CBME into medical education systems [ 5 ]. Recognizing this challenge, the concept of Entrustable Professional Activities (EPAs) has emerged.

EPAs are essentially tasks or activities that medical professionals should be able to perform competently and independently by the time they complete their training [ 6 , 7 ]. EPAs are used to assess a learner’s ability to integrate and apply the necessary competencies in real-world clinical practice. They necessitate evaluating a learner’s progress and readiness for independent practice by observing their performance in these key professional activities in clinical practice [ 8 ]. The term “entrustable” indicates that, upon graduation or completion of a specific training period, a supervising physician or mentor should be able to entrust a medical graduate with these activities without direct supervision, considering them proficient and safe for the patients to perform these tasks independently [ 9 , 10 ].

Considering the immense potential, integration and implementation of EPAs has gained rapid momentum, across various health professions and medical specialties [ 11 , 12 ]. Despite this progress, a significant gap notably persists, when it comes to accommodating competing needs of curriculum designers and those of users in practice, namely trainers and trainees [ 13 ]. While the promise of EPAs in facilitating CBME is promising, there is lack of comprehensive evidence incorporating users’ perceptions during the design phase [ 8 , 11 , 14 ]. Therefore, the aim of this study was to design an EPA framework for workplace-based assessment by actively involving clinical educators, trainees and trainers throughout the process.

Setting and participants

This study took place in the interuniversity postgraduate General Practitioner’s (GP) Training, Belgium. To standardize GP Training across Flanders, four Flemish universities (KU Leuven, Ghent University, University of Antwerp, and the Flemish Free University of Brussels) collaboratively developed a postgraduate training program. This training program consists of three different training-phases and rotations, spread through three years, two rotations are in a GP practice, while one takes place at a hospital setting.

The GP Training is overseen by the Interuniversity Centre for GP Training (ICGPT). The ICGPT plays a pivotal role in coordinating and managing various aspects of the curriculum. Among its key responsibilities, the ICGPT oversees the allocation of clinical internships, conducts examinations, facilitates regular meetings between trainees and trainers, and maintains trainees’ learning electronic (e-) portfolios.

In 2018, the ICGPT initiated a shift towards CBME. The rationale of CBME was introduced in the curriculum by integrating first the CanMEDS roles. To facilitate this transition, two clinical competence committees (CCCs), comprising medical doctors and clinical educators from the four universities were appointed. These CCCs were tasked with coordinating workplace-based learning, and curriculum and assessment, respectively.

To align the curriculum with the patient needs in primary care, the two CCCs designated and defined ten different care contexts characteristic of primary care (i.e. short-term care, chronic care, emergency care, palliative care, elderly care, care for children, mental healthcare, prevention, gender related care, and practice management). Subsequently, in 2022, we initiated the process of designing specific EPAs for the GP Training. The EPAs aimed to facilitate and improve workplace-based assessment. These two CCCs participated in the design process, while trainers and trainees were invited to share their opinion as well.

Designing the EPA framework

The design of the EPA framework was based on participatory research design to engage different stakeholders [ 15 ]. Participatory research design is a community-based methodology aiming to create solutions for and with the people who are involved [ 15 ]. This iterative research approach encompassed three fundamental design-stages in a circular relationship, namely design, evaluation and refinement (Fig.  1 ). We executed two distinct iterative cycles, each with a specific group of stakeholders (Fig.  2 ). In cycle 1, we focused on CCCs, fostering discussions and validating the framework. In cycle 2, we involved clinical trainers and trainees, ensuring cross-validation. In the following section, we describe each iterative cycle, indicated as cycle 1 and as cycle 2, respectively.

Three design phases for designing the EPA framework

Process for developing the EPA framework based on participatory design research

In cycle 1, after reviewing relevant literature, we developed a blueprint of 60 EPAs corresponding to the ten different care contexts, already integrated in the curriculum [ 9 , 10 ]. By doing so, we wanted to ensure practical applicability and relevance of our framework within the established educational environment. Afterwards, we linked all EPAs to the CanMEDS competency framework [ 16 ]. We defined competencies as broad statements that describe knowledge, skills and attitudes that GP trainees should achieve during the different training phases [ 17 ]. The CanMEDS framework identifies and describes different competencies for patient-centred care, and comprises seven different roles: medical expert, communicator, collaborator, leader, health advocate, scholar, and professional. By linking EPAs to CanMEDS, we constructed a matrix that served as a structured guide for integrating the EPAs in the workplace. Also, together with the CCCs we defined behavioural and cognitive criteria to anchor entrustment levels [ 9 ]. These criteria described required knowledge, skills, and attitudes in order for an EPA to be entrusted.

In cycle 2, we aimed at operationalising the EPAs, cross validating them by interviewing trainers and trainees, and deciding entrustment levels. Specifically, to operationalise the EPAs, we developed an assessment form, called Clinical Practice Feedback form (Fig.  3 ). We chose to link EPA assessments not only to direct and video observations, but also for case-based discussions. Additionally, we agreed upon entrustment levels and the entrustability scale. Entrustment was anchored on criteria that were defined along the EPAs. We decided to use the Ottawa Surgical Competency Operating Room Evaluation (O-SCORE) for validity and reliability reasons (Fig.  4 ) [ 18 ]. The Ottawa scale requires assessors to describe how much supervision they provided to trainees while performing a specific EPA. Concretely, the scale comprises five levels of performance ranging from trainers taking over the activity to trainees performing the activity without supervision (Fig.  3 ) [ 18 ].

Example of Clinical Practice Feedback form available in the e-portfolio

Five levels of entrustment based on the O-SCORE scale [ 19 ]

Data collection and analysis

In cycle 1, we evaluated the EPA blueprint by employing a modified Delphi methodology, with two rounds [ 19 ]. We invited members of the two CCCs ( N  = 14) to give feedback on the EPA blueprint via e-mail and during meetings, scheduled by the ICGPT. Members were asked whether they thought each EPA was necessary for workplace-based assessment and needed to be included in the framework. They were also encouraged to give feedback regarding the formulation of the EPAs. Once we gathered all the comments, we refined the blueprint and sent it back to the CCC members. In cycle 2, we interviewed two trainers and two trainees using semi-structured interviews and following the ‘think-aloud protocol’ [ 20 , 21 , 22 ], where we asked them whether each EPA was necessary and whether they were comprehensible for workplace-based assessment. Participants were required to articulate their thoughts while reading the EPA framework. This enabled us to gain insights into their thought processes and perspectives [ 22 ].

Data collection took place from February 2022 until September 2022. For quantitative data analysis we calculated descriptive statistics of consensus rates using SPSS 27 (IBM SPSS Statistics 27). We analysed qualitative data from CCCs members using content analysis on Microsoft Excel. For analysing data from the interviews with the trainers and trainees, we first verbatim transcribed the interviews, and, then, analysed the data using thematic analysis in NVivo (QSR International) [ 23 , 24 ]. Qualitative data were analysed by two researchers separately to achieve triangulation, while a third researcher was consulted, when discrepancies arose [ 25 ].

Reflexivity and research team

The research team was composed of members with different backgrounds. Two members had a background in education, while the other two members had a background in biomedical sciences and general practice. All authors had research training and experience in medical education research. Methodological and design decisions were in line with the available literature. We predefined methodological steps before commencing the study. To ensure adherence to our design stages, we maintained a detailed logbook to document systematically progression and modifications from our initial protocol. We regularly discussed the results to ensure that our interpretations were close to the data.

In cycle 1, fourteen members of the CCCs gave feedback on the list of 60 EPAs. In the first feedback round, all members agreed that all 60 EPAs were required in the framework. Twenty comments were given regarding the formulation of the EPAs and 16 adaptations were made based on the new suggestions. Comments regarding the formulation were about the use of certain words in order to make the framework understandable. In the second feedback round, consensus was reached on the formulation of the EPAs (Table  1 ).

In cycle 2, we interviewed two trainers and two trainees. CCC members, trainers, and trainees agreed that all EPAs should be included in the framework. From these interviews, we identified three themes. Table  2 presents these three themes alongside their subthemes. Necessity of EPAs was the first theme and included shared mindsets about necessity of EPAs in order to improve workplace-based assessment and difficulties with interpreting the CanMEDS roles.

“ The EPAs are better than the CanMEDS. My trainer and I often do not know what we have to assess…He (the trainer) sometimes gives the same feedback for multiple roles .” (trainee 1).

Second theme was about the relevance of EPAs to clinical practice. Users thought that the EPA framework could easily be linked to their clinical work, promoting assessment and feedback opportunities. They agreed that EPAs were understandable and formulated in intuitive language for clinical work.

“ I think that it (the EPA framework) is quite intuitive. I can see a lot of links between the EPAs and my daily practice .” (trainer 2).

I like the (EPA) framework. My trainer and I already discuss some of these (activities) during our weekly feedback session . (trainee 2)

Third theme included challenges in implementation of EPAs, regarding the large number of EPAs, perception of high-stakes assessment within an e-portfolio, and limitations inherent to the current e-portfolio. First, users expressed their concern regarding the large number of EPAs. They indicated that only a limited number might be feasible because of time constraints in the clinical workplace. Also, users thought that due to the large number of EPAs, trainees would “pick and choose” EPAs where they had performed well. Along with limited functionalities of the current e-portfolio, they indicated that EPAs might be used as showcasing performance instead for workplace-based assessment and feedback purposes. Mainly trainees expressed hesitation to document EPAs where they would need further improvement. They perceived the e-portfolio as a tool more suitable for high-stakes assessments rather than for feedback purposes.

“ The list (of EPAs) is quite extensive… I do want to have a nice portfolio, so for sure I will try to include as many as possible. In case something happens (in my curriculum), I want to show how well I have been performing .” (trainee 1).

“ I normally do not include patient cases that went wrong in my portfolio. Because different people have access to it (the e-portfolio).” (trainee 2).

The aim of this study was to design an EPA framework by actively engaging and collaborating with different stakeholders. To be established as a “good” assessment framework, EPAs should be acceptable by the different stakeholders involved in the assessment process, such as curriculum designers, trainees and trainers [ 26 , 27 ]. Incorporating their opinions and understanding their different needs must be integral to the design process. However, literature regarding EPAs design has mainly focused on experts’ opinion, neglecting users in practice [ 8 ].

From our findings, it becomes apparent that direct involvement and communication among diverse stakeholders are crucial for designing a useful for everyone EPA assessment framework. When various groups are involved in developing educational interventions, competing needs can be optimally addressed [ 28 ]. This optimization fosters a cohesive approach, ensuring high applicability rates and effectiveness, when the EPA framework is used in practice. The need for users’ involvement in the development process is currently demonstrated in the most recent EPA literature [ 29 , 30 ]. Users’ involvement promotes common language and expectations, enhancing the clarity and effectiveness of EPA interventions, and, most importantly, empowers the users themselves by acknowledging their perspectives [ 31 ]. Ultimately, trainees and trainers are the ones using the EPA assessment frameworks during daily clinical practice, and are potentially confronted with unforeseen obstacles.

Additionally, users’ involvement in the process can help to identify potential implementation challenges [ 32 , 33 ]. Our findings indicate differences in opinions regarding implementation of EPAs. In contrast to the CCC members, users expressed their concerns about the large number of EPAs included in the framework. They were particularly concerned about how to use sufficiently and adequately EPA assessments, while juggling clinical work. This concern echoes findings from other studies as well, related to the assessment burden [ 34 ]. In particular, when challenges in assessment processes arise in the clinical workplace, assessment is most probably not performed as intended [ 35 ].

Furthermore, our results illustrate tensions between assessment of learning and assessment for learning. Although the EPA assessments aim to better prepare trainees for clinical practice, users suggested that the purpose of the EPAs might not be explicit for everyone. Since EPAs are a form of assessment, they could potentially lead to strategic behaviours of documenting successful EPAs, and, therefore, creating a fragmented idea about trainees’ performance in clinical practice. Additionally, the use of the current e-portfolio for high-stakes assessments only adds to this tension. Especially, trainees were not comfortable with sharing performance evidence for improvement, because they perceived the stakes as high [ 36 ]. The dilemma between learning versus performing has been the Achilles point in workplace-based assessment [ 37 ]. The lines between assessment and feedback seem to be also blurred in EPAs [ 38 , 39 ].

Involving users during the design process can lead not only to early adaptations and refinement of EPAs, but also to better allocation of resources. In order to ensure successful implementation of EPAs, it is essential to recognize the central role of both trainers and trainees. Future research should focus on training programs designed to equip faculty, trainers, and trainees with a profound understanding of EPAs. Users in practice need rigorous training covering EPA principles, assessment techniques, and feedback strategies [ 40 ]. Moreover, fostering a culture of interdisciplinary collaboration among stakeholder groups is imperative. Encouraging review of assessment tools and facilitating the exchange of opinions during designprocesses can significantly enhance the overall quality of EPA frameworks, and, even more broadly, of workplace-based assessment practices.

Although EPAs are a valuable framework for assessing competencies in workplace settings, integrating other assessment tools is crucial to capture the full spectrum of skills needed to meet patient needs. Future research should focus on combining EPAs with other assessment methods, such as simulation-based assessments, either with standardized patients or with virtual reality, that would allow trainees to demonstrate their clinical and interpersonal skills within safe, controlled environments that closely replicate challenging patient scenarios [ 41 ]. Additionally, incorporating multisource feedback and continuous portfolio assessments could offer a comprehensive view of a trainee’s performance across various settings and interactions [ 42 , 43 ]. Together, these methods would enhance the EPA framework, ensuring a comprehensive assessment of all essential competencies that future physicians should acquire.

Limitations

We need to acknowledge several limitations in this study. First, in medical education research, users’ involvement prerequisites a degree of experience with a specific subject. In our study, we involved users in the early design process of the EPA framework. Although we are aware of this limitation, we intentionally and consciously chose a participatory research design. We believe that users are experts in their own experience, and that they hold the knowledge and capabilities to be involved as partners in the development process. Second, our study involved a low number of users due to difficulties in recruitment. This might have led to recruiting participants who are fully engaged in the educational practices of the GP Training. Nevertheless, our findings are rooted in two methodologies, namely a modified Delphi method and semi-structured interviews. Therefore, we used triangulation to verify our results [ 25 ]. Finally, although workshops are mostly commonly in co-design studies [ 44 ], our study coincided with the last COVID-19 lockdown, necessitating adjustments. To cope with these challenges and uncertainties, we opted for methods that were the most feasible for our participants at that moment. Despite these challenges, the contributions from all stakeholders were invaluable, particularly in exploring potential implementation and evaluation issues.

For EPAs to be successful, they need to be acceptable as an assessment framework by different stakeholders’ groups. Accommodation of competing stakeholders’ needs during the design process is crucial for enhancing acceptability and effectiveness during implementation. Our findings highlight the significance of collaborative efforts to design EPAs, emphasizing its potential to empower users, identify implementation barriers, and pinpoint unintended consequences. Through this collaborative approach, wherein diverse stakeholders contribute their perspectives, we can create effective educational solutions to complex assessment challenges.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

General Practitioner

competency-based medical education

Entrustable Professional Activity

Canadian Medical Education Directives for Specialists

Interuniversity Centre for GP Training

clinical competence committee

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Acknowledgements

The authors would like to acknowledge the contribution of Mr. Guy Gielis, Mrs. An Stockmans, Mrs. Fran Timmers, and Mrs Karolina Bystram that assisted with coordination of the CCCs. We would also like to thank and acknowledge Prof. dr. Martin Valcke and Dr. Mieke Embo for facilitating this study through the SBO SCAFFOLD project(www.sbo-scaffold.com). Finally, we would like to thank the CCCs members and the trainers and trainees that participated in this study.

This work was supported by the Research Foundation Flanders (FWO) under Grant [S003219N]-SBO SCAFFOLD.

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Andreou, V., Peters, S., Eggermont, J. et al. Co-designing Entrustable Professional Activities in General Practitioner’s training: a participatory research study. BMC Med Educ 24 , 549 (2024). https://doi.org/10.1186/s12909-024-05530-y

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• Published: 18 May 2024

Research on trajectory control technology for L-shaped horizontal exploration wells in coalbed methane

• Xiugang Liu 1 , 2 , 3 ,
• Zaibing Jiang 1 , 2 , 3 ,
• Yi Wang 3 ,
• Haitao Mo 3 ,
• Haozhe Li 3 &
• Jianlei Guo 3  

Scientific Reports volume  14 , Article number:  11343 ( 2024 ) Cite this article

Metrics details

• Energy science and technology
• Engineering

Horizontal wells have significant advantages in coal bed methane exploration and development blocks. However, its application in new exploration and development blocks could be challenging. Limited geological data, uncertain geological conditions, and the emergence of micro-faults in pre-drilled target coal seams make it hard to accurately control the well trajectory. The well trajectory prior to drilling needs to be optimized to ensure that the drilling trajectory is within the target coal seam and to prevent any reduction in drilling ratio (defined here as the percentage of the drilling trajectory in the entire horizontal section of the well located in the target coal seam) caused by faults. In this study, the well trajectory optimization is achieved by implementing the following process to drill pilot hole, acquire 2D resonance, and azimuthal gamma logging while drilling. The pilot hole drilling can obtain the characteristic parameters of the target coal seam and the top and bottom rock layers in advance, which can provide judgment values for the landing site design and real-time monitoring of whether the wellbore trajectory extends along the target coal seam; 2D resonance exploration can obtain the construction of set orientation before drilling and the development of small faults and formation fluctuations in the horizontal section, which can optimize the well trajectory in advance; the azimuth gamma logging while drilling technology can monitor the layers drilled by the current drill bit in real time, and can provide timely and accurate well trajectory adjustment methods.The horizontal well-Q in the Block-W of the Qinshui Basin was taken as a case study and underwent technical mechanism research and applicability analysis. The implementation of this new innovative process resulted in a successful drilling of a 711 m horizontal section, with a target coal seam drilling rate of 80%. Compared to previous L-type wells, the drilling rate increased by about 20%, and the drilling cycle shortened by 25%. The technical experience gained from this successful case provides valuable insight for low-cost exploration and development of new coalbed methane blocks.

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Introduction.

Coal Bed Methane (CBM) is found in many parts of the world, and is considered as a clean and abundant source of energy 1 , 2 , 3 . In general, CBM wells mainly include three types; vertical, cluster and horizontal wells. The cluster and horizontal wells belong to directional wells. Moreover, horizontal wells could be further classified into; V-, U- and L-shaped wells. Which in turn could also be divided according to their radius, and branches. Figure  1 below provide an illustration for some of these wells.

Illustration of well types; ( a ) Vertical well, ( b ) Cluster well, ( c ) Horizontal Well, and ( d ) Horizontal L-Shaped well with a vertical well forming a U-Shaped well.

In the development of CBM wells, L-shaped, U-shaped and multi-branch horizontal wells are usually used for new exploration and development blocks (defined here as new fields or area blocks in the oil and gas industry) 4 , 5 , 6 . However, complex formation structure, and small faults development have made it an extremely challenging task to achieve high output from newly developed CBM wells 7 . For instance, U-shaped wells (a well type in which a vertical well and a horizontal well are connected in the same target layer) face huge difficulties in accurate docking along the coal seam and have limited benefits in the presence of multiple faults in the horizontal Section 8 . Similarly, the applicability of multi-branch horizontal wells is poor, especially in complex stratigraphic structures and fault development of the block 9 .

On the other hand, L‑shaped horizontal wells are often adopted as the main type of wells for exploring and developing CBM in new blocks. The L-shaped horizontal wells exhibit uncomplicated drilling prerequisites, demonstrate a low probability of wellbore collapse or obstruction, and facilitate subsequent access for maintenance of the initial wellbore 10 . However, the drilling process of these wells are not free of challenges. L-shaped wells have a high requirement for wellbore trajectory control, and they are usually difficult to achieve one-time “soft landing” and ultra-long horizontal segment footage 10 . In addition, drainage equipment and method are another key restriction for the promotion and application of this type of well 11 . For example, reported completion data from several exploration wells indicated that the drilling ratio along the coal seam of the actual trajectory is less than 60%. The drilling cycle is nearly two months, and gas production is low 11 . Table 1 illustrates a tabulated analysis of the applicability and challenges associated with different well types in exploration blocks characterized by complex geological formations and the presence of micro-faults.

Various methods have been used to improve the drilling ratio, by improving the trajectory control. These methods, shown in Table 2 , include: geological guidance technology of adjacent well data, electromagnetic waves, natural gamma measurement, and three-dimensional seismic exploration technology. However, each method has its own limitations, such as high costs, difficulty in obtaining gamma values in specific directions, and signal loss when applied to drilling in complex formations 12 , 13 .

This study delves into trajectory control methods for Horizontal wells within Coalbed Methane (CBM) exploration and development blocks. The approach involves the utilization of pilot holes to determine the characteristics of the target coal seam and the surrounding upper and lower rock layers based on the magnitude of gamma values. This information serves as a predictive identification of marker layers, allowing real-time control and adjustment of the drilling trajectory within the target coal seam. This methodology enables the identification of whether the drilling trajectory is presently positioned within the target coal seam, the roof rock layer, or the floor rock layer. Additionally, a two-dimensional resonance exploration technology is employed for geological structure and fault detection prior to drilling, enabling pre-drilling trajectory optimization. Furthermore, azimuth gamma logging technology is utilized for real-time monitoring and correction of the drilling trajectory's horizontal positioning during the drilling process. Using L-shaped Short-Radius Well-Q in Block-W of the Qinshui Basin as a case study, a comprehensive assessment of the combined effectiveness of these three methods is conducted. Simultaneously, the research delves into the technical mechanisms and applicability analysis. This exploration of the technical mechanisms aims to enhance the understanding of the functions of these methods, their application conditions, and the analysis and utilization of their technical effects.

Trajectory control methodology

Pilot hole drilling, construction background and reasons.

The area formation structure and faults nature could be obtained by two-dimensional seismic data. Seismic surveys and exploratory drilling in the area could provide a good indication on the coal seam actual depth, coal seam distribution, layers, belts and interbeds. For the geological conditions of developing new blocks, such as less drilling data, less seismic exploration data, complex formation structure and micro-fault development, etc., before drilling, it is imperative to obtain the key parameters of the target coal seam, including its lithology, gas-bearing capacity, gamma value, etc., along with those of the rock layers above and below it. This will allow for the determination of the precise horizon of the coal seam and provide technical support for real-time monitoring and well trajectory control along the target coal seam. To achieve this, it is necessary to design and implement a pilot hole drilling program to obtain the characteristic parameters of the target coal seam and the surrounding strata 14 , 15 .

Pilot hole construction design

Once the goal of layer identification is achieved, the next step is to backfill and sidetrack the pilot hole to open branches and land according to the actual occurrence of the coal seam. To ensure the effectiveness of the pilot hole guidance in subsequent construction, it is advisable to minimize the distance between the coal-seem top point (the point where the drilling trajectory first drills into the target coal seam) and the landing point by increasing the well angle of inclination. Conversely, in order to enhance the construction efficiency of the pilot hole, it is preferable to keep the depth of the pilot hole to a minimum, which is indicated by a small well angle of inclination (70 degrees). Figure  2 illustrates this concept.

Optimization of pilot hole scheme.

Taking into account the underlying reasons and background for constructing a pilot hole, as well as the difficulty of side-tracking and the efficiency of construction, a comprehensive plan has been developed. The plan involves drilling the pilot hole at a steady angle of approximately 70° until the bottom of the target coal seam is reached.

• Two-dimensional resonance exploration

Resonance exploration mechanism

The seismic wave frequency resonance exploration technology is a novel geophysical exploration method that utilizes the frequency resonance principle prevalent in nature to investigate underground geological formations 16 , 17 , 18 , 19 . This technique enables the acquisition of geometric attributes of subsurface structures, such as fractures and faults. Figure  3 illustrates a typical resonance diagram of a seismic wave.

( a ) Typical resonance curve of seismic wave ( b ) self-excite resonance to vibration.

Resonance exploration technology boasts numerous advantages, including high sensitivity to density changes, exceptional vertical and horizontal resolution, and an exploration depth of up to 5000 m. Additionally, this technology can be acquired and processed passively, making it an economical and straightforward exploration method 20 .

Analysis of technical applicability

At this stage, the analysis of the existing two-dimensional seismic data in the exploration block would indicate the geological structure of the target coal seam in the block. In addition, it will reveal fault’s locations beside faults development status. The pilot hole drilling can accurately obtain the actual depth of the target coal seam and the characteristic parameter values of the target layer, as well as the roof and floor, but conventional means cannot predict structural conditions such as the development of micro faults in the horizontal section of the drilling along the designated direction. This increases the difficulty of well trajectory control and makes it challenging to ensure the coal seam drilling ratio. However, the two-dimensional resonance exploration technology can be used to infer the development of small faults in the horizontal section drilled along the specified direction by interpreting the resonance image. This enables the optimization of the well trajectory in advance to control the actual drilling trajectory and improve the drilling rate of the target coal seam.

Azimuth gamma control technology

Working principle of azimuth gamma.

The azimuth gamma logging tool is utilized to measure the width of gamma ray energy level 21 , 22 , 23 . The scintillation counter captures gamma rays from the stratum, and azimuth gamma logging while drilling offers unique advantages 24 , 25 . Firstly, it enables real-time calculation of the strata's apparent dip angle. It is convenient to calculate the apparent dip angle of the strata by utilizing the azimuth gamma data. The apparent dip angle at the current position can be obtained as long as it is required to cross an interface. The formula for calculating the apparent dip angle using the azimuth gamma 26 is as follows:

where α is the apparent strata dip; D is the well diameter; Δd is the distance between the upper and lower gamma value change points; β is the well deviation angle.

Second, measuring the natural gamma value in a specific direction. By transmitting up and down gamma data in real-time, it becomes possible to accurately determine the positions of different formation interfaces 27 , 28 . This information can then be used to ensure that the trajectory of the control well is precisely aligned with the target coal seam after drilling is complete. The specific process involved is illustrated in Fig.  4 .

Trajectory control based on azimuth-while-drilling gamma logging. ( a ) Coal seam drilled out from the roof. ( b ) Coal seam drilled out from the floor.

The drilling process in the horizontal section along the coal seam is susceptible to deviate from the target due to increased drilling pressure or the impact of the formation structure. The strata above and below the coal seam are usually mudstone or carbonaceous mudstone. When using azimuth gamma logging during drilling, the upper gamma value first increases, followed by the lower gamma value, indicating that the drilling has exited the coal seam roof at point C in Fig.  4 a. When the upper and lower gamma values become similar, it suggests that the drilling has left the layer, as shown at point D in Fig.  4 a. To correct the inclined drilling control track deviation, the trajectory correction process is initiated when drilling to point C using azimuth gamma measurement, as demonstrated at point E in Fig.  4 a. Similarly, when the lower gamma value increases first and the upper gamma value increases later, it indicates that the drilling trajectory is exiting the coal seam floor at point C1 in Fig.  4 b. When the upper and lower gamma values become similar, the drilling has left the layer, as shown at point D1 in Fig.  4 b. To correct the incremental drilling control track deviation, the trajectory correction process is initiated when drilling to point C1, as illustrated at point E1 in Fig.  4 b.

In terms of technical applicability, conventional natural single gamma logging technology cannot accurately determine the bit's position once it leaves the coal seam, making it challenging to provide precise corrective measures. This issue is particularly problematic wherever the geological structure of the target coal seam is complex, micro faults are developed, and the coal seam is thin. To ensure the penetration ratio of the target coal seam and ensure the safety of underground construction, azimuth gamma logging while drilling technology can be utilized. This technology allows for the real-time monitoring of the current drilling horizon and provides effective guidance during construction. As a result, the drill bit can efficiently drill into the coal seam, maximizing the penetration ratio of the target coal seam.

Technical applicability analysis

In the second drilling operation, if the targeted coal seam is complex due to its thinness or the presence of micro-faults, it will be very challenging to accurately determine the position of the drilling bit after it exits the coal seam. Therefore, it will be necessary to use azimuth gamma logging while drilling. This technology enables the real-time monitoring of the drilling bit's current horizon, guiding the construction process and ensuring that the bit drills to the maximum extent possible within the coal seam.

Trajectory control technology and case study

Geological setting.

In this study, the short radius, well-Q in Block-W of the Qinshui Basin is taken as an example. Based on the most recent exploration wells drilled in Block-W of Qinshui Basin, the geological horizons have been revealed. The strata in the block, from bottom to top, consist of Paleozoic Ordovician, Carboniferous, Permian, Mesozoic Triassic, Jurassic, and Cenozoic Quaternary. The stratum near Well-Q has a general inclination from northeast to northwest, and Coal Seam no.15 is the development target stratum. The coal seam is located in the lower part of the Taiyuan Formation and has a simple structure. It is a thick coal seam that is stable and easy to drill throughout the area and generally contains 0–2 layers of dirt shale. The effective thickness of the coal seam ranges from 0 to 5.30 m, with an average of 3.39 m. It is thicker in the east and thinner in the west. However, there is one exploration well in the block that did not drill into Coal Seam no.15, possibly due to fault interference resulting in the loss of the coal seam. The coal seam deposit depth ranges from 728 to 2002 m, with an average of 1479 m. The depth is shallow in the southeast of the block and gradually deepens towards the northwest. Due to the influence of the stratum tendency (Stratum dip), the depth of the coal seam reaches over 1500 m in the west 14 . The roof lithology of the coal seam mostly consists of sandy mudstone, mudstone, siltstone, and fine sandstone, while the floor is mostly sandy mudstone, mudstone, and siltstone.

Wellbore structure

Designing an optimized wellbore structure can greatly improve drilling efficiency and safety by reducing annular pressure loss and back pressure (the drilling tool back pressure phenomenon), especially for long well sections. In the case of Well-Q, the wellbore structure was designed with a three-opening sections to ensure gas production of the coal seam during subsequent fracturing development. The first section seals the formation prone to collapse and leakage in the upper part of the primary casing, creating a safe drilling environment for the second well section. The second section seals sandstone, mudstone, and sandy mudstone intervals at the upper part of the coal seam, with the second well section casing obliquely drilled to a depth of no less than 3 m from the target coal seam no.15.

The third section extends along coal seam no.15 and runs casing to form a stable gas production channel to prevent coal seam collapse in the horizontal section due to the influence of multiple factors such as fracturing in the later stage. Prior to drilling the second well section of the main borehole, pilot hole drilling was carried out to obtain relevant geological parameter information of the target coal seam and the adjacent marker bed. Specific design parameters and requirements are as follows:

In the first well section, a ø 346.1 mm drill bit was used to drill into the stable bedrock for 30 m. J55 grade steel ø 273.1 mm surface casing was then lowered and cementing cement slurry returned to the surface.

In the second well section, a ø 241.3 mm drill bit was used to drill to the roof of the target no.15 coal seam and then the drilling was stopped. The landing point was determined based on the lithology of the roof of the coal seam and the actual drilling process. N80 grade steel ø 193.7 mm technical casing was run to 3–5 m above the roof of the coal seam. Through variable density cementing process, high-density cement slurry was used to return to 300 m above the roof of Coal Seam no.15, while low-density cement slurry returned to the surface.

The third well section was drilled with a ø 171.5 mm drill bit. After entering the target coal seam no.15, the drilling followed the coal seam. Upon reaching the designed well depth, P110 grade steel ø 139.7 mm production casing was run, and the well was completed without cementing.

The pilot hole was drilled with a ø215.9 mm bit, and the inclination angle stabilizing drilling crossed the floor of the target coal seam for tens of meters. Subsequently, the bit was backfilled with pure cement slurry to the side drilling depth of the second well section. The specific wellbore structure is shown in Fig.  5 .

Well structure.

Case study: well-Q design optimization

Using Well-Q as a case study, the pilot hole trajectory design included the following: straight well section, kicking-off section, and stabilizing section. The stabilizing drilling passes through the floor of Coal Seam no.15 for approximately 30 m at an inclination angle of 70° to ensure accurate measurement of the gamma value, gas measurement value, and other characteristic parameters of the target coal seam bottom and floor using a simple gesturing instrument. The pilot hole is sealed by backfilling it with 42.5 grade Portland cement up to the well section with an inclination of about 25°, and the cement slurry has a specific gravity of 1.6–1.7 g/cm3. As the well deviation angle increases, the azimuth angle of directional and composite drilling becomes more stable, particularly when the well deviation angle exceeds 25°, resulting in a smaller azimuth drift 29 . This stability is beneficial for the subsequent inclined side-tracking in the main wellbore's second well section. The pilot hole and main borehole design trajectories are shown in Fig.  6 .

Design trajectory of pilot hole and main hole.

Significant data has been obtained through the pilot hole design and the actual drilling of Well-Q. This dataset is pivotal for precise trajectory control in Coalbed Methane (CBM) exploration. The acquisition process relies on several methods, including real-time drilling natural gamma logging for gamma values of marker layers, and downhole gas logging for coal seam gas characteristics. The examination of cuttings recorded in real-time during drilling operations further aids in the identification and differentiation of these marker layers.

The critical information gleaned encompasses the identification of the K2 marker bed, the longitudinal stratification of the target no.15 coal seam, as well as the lithological composition, gamma values, and gas-bearing attributes of the upper and lower rock layers. These specific parameters are thoughtfully presented in Fig.  7 , establishing a robust foundation for the meticulous control of trajectory and the rational design of the landing point within the target coal seam. This dataset also serves as a valuable point of reference, ensuring the seamless execution of the horizontal drilling phase within the coal seam. Consequently, these findings play a pivotal role in enhancing drilling efficiency, ultimately culminating in the realization of efficient drilling objectives.

Characteristic parameters and lithology map of the marker layer, target, top, bottom layer.

The effect of two-dimensional resonance method

The horizontal section's overall drilling azimuth in the target coal seam is 200°. To identify minor faults in the coal seam azimuth direction, measurement points are arranged every 10 m from the landing point A to the final target point B along the 200° azimuth direction. Additionally, one exploration point is set every 20 m across the azimuth line perpendicular to the landing point A and 200° azimuth direction. Furthermore, exploration points are arranged 300 m along both sides of the landing point. Figure  8 shows the specific layout of the exploration points, where Line (L1) represents the 711 m long horizontal well section of the target coal seam in the 200° azimuth direction. Meanwhile, Line (L2) represents the 600 m long vertical section between the landing point A and L1. The obtained data from these exploration points are crucial in detecting potential faults and ensuring smooth drilling of the horizontal section of the coal seam. ultimately leading to improved drilling ratios and more efficient drilling.

Two-dimensional resonance exploration layout points.

Figure  9 shows the seismic frequency resonance inversion profile. The trajectory of the designed horizontal section coincides with the ground position of L1, with the no.4700 measuring point located at the ground projection position of the A target point, and the no.4000 measuring point located at the ground projection position of the B target point. Based on the interpretation of seismic frequency resonance line L1 profile, it is observed that the burial depth of the coal seam on the horizontal well section from target A to target B of the no.15 coal seam in the direction of 200° azimuth is shallow in the northeast and deep in the southwest. The overall trend of the burial depth of the coal seam indicates a shallow-to-deep trend. Furthermore, three small faults are expected to be encountered while drilling along this azimuth direction, located at no.4700, no.4280 and no.4096 measuring points, respectively, with a fault distance of approximately 5–10 m.

Design of horizontal section trajectory resonance exploration inversion profile.

The contour map of fault points found in the horizontal section is displayed in Fig.  10 . This map serves as a useful tool in guiding the vertical depth control of the horizontal section track.

Contour map of fault points in the horizontal section.

To ensure that the drilling trajectory is within the target coal seam and to prevent any reduction in drilling ratio caused by the faults, it is necessary to optimize the well trajectory prior to drilling. Each fault point must be considered as a target point and their relative coordinate positions are presented in Table 3 .

Resonance exploration data is utilized to adjust the trajectory parameters every 10 to 20 m during the actual drilling process. This is before exploring the coal seam behind the fault following reasonable adjustment of the parameters. This method is simple and minimizes the length of the non-coal section during the coal chasing process after drilling through the fault. Based on the coordinate position of each target point, the design of the directional trajectory for the third well section is optimized, as shown in Fig.  11 .

optimized well trajectory for drilling reservoir section. ( a ) vertical section, ( b ) horizontal projection section.

The optimized design trajectory should be followed during actual drilling, ensuring that the dogleg degree ≤ 4°/30 m required by the management method for safe operations. Across the fault points F1, F2, and F3, the length of the non-coal section for coal tracking drilling was 56 m, 53 m, and 35 m, respectively. The total non-coal section for actual drilling was approximately 144 m, while achieving a drilling ratio of 80% for the target coal seam with an average thickness of 2.06 m. The entire drilling cycle takes approximately 45 days.

Azimuth gamma application

By analyzing the azimuth gamma data obtained during the drilling of the pilot hole and using the basic parameters of the pilot hole and formula ( 1 ), the apparent dip angle of the stratum near the designed landing point is determined to be α = 6.5°. The parameters of the landing point are shown in Fig.  12 , and the deviation angle of the actual main borehole trajectory of the second well section at the landing point β should be controlled at around 83.5° to ensure that the drilling ratio along the coal seam of the third well section is achieved and to reduce the frequency of directional trajectory adjustment.

Parameters of the landing site.

During the drilling of the third horizontal section of Well-Q, a combination of Two-dimensional resonance exploration results and azimuth gamma logging while drilling technology was used to guide rapid coal tracking during the drilling of three faults. The process for each fault was as follows:

F1 Fault: The logging curve in Fig.  13 indicates that the F1 fault caused the drilling track of the 1920–1976 m well section to be drilled out from the coal seam roof. Geological logging revealed that the rock debris returning out of the hole bottom contained a large amount of mudstone. Based on the Two-dimensional resonance exploration inversion (Fig.  9 ) and fault contour (Fig.  10 ), the coal seam was traced by drilling with deviation correction through the lowering of well deviation. The actual drilling track during the pursuit of coal process is shown in Fig.  14 .

Non-coal seam section azimuth gamma logging curve crossing fault F 1 .

Actual drilling trajectory of fault F 1 in pursuit coal.

F2 Fault: The logging curve in Fig.  15 shows that the F2 fault caused the drilling trajectory of the 2130–2183 m well section to be drilled out from the coal seam roof. Geological logging revealed that the rock debris returning out of the hole bottom contained a large amount of mudstone. Based on the Two-dimensional resonance exploration inversion (Fig.  9 ), the back fault block of F2 fault in the direction of drilling trajectory of F2 fault shows a tendency of coal seam incline, so directly using lowering deviation correction drilling to trace the coal seam is not feasible and increases the length of the non-coal seam section. Therefore, the coal seam was pursued by increasing well deviation and rectifying drilling. The actual drilling track during the pursuit of coal process is shown in Fig.  16 .

Non-coal seam section azimuth gamma logging curve crossing fault F 2 .

Actual drilling trajectory of fault F 2 in pursuit coal.

F3 Fault: The logging curve in Fig.  17 shows that the F3 fault caused the drilling trajectory of the 2315–2350 m well section to be drilled out from the coal seam roof floor. Geological logging revealed that the rock debris returning out of the hole bottom contained a large amount of carbonaceous mudstone. Using formula ( 1 ), the coal point well inclination angle was calculated as 96°. Based on the Two-dimensional resonance exploration inversion (Fig.  9 ) and fault contour (Fig.  10 ), the coal seam was pursued by slowly lowering the well inclination and correcting the deviation. The actual drilling track during the pursuit of coal process is shown in Fig.  18 . The well inclination angle was 91° upon returning back to the coal seam, after which drilling along the coal seam was continued normally.

Non-coal seam section azimuth gamma logging curve crossing fault F 3 .

Actual drilling trajectory of fault F 3 in pursuit coal.

In conclusion, for the exploration block of CBM, the combined use of pilot hole drilling, two-dimensional resonance exploration technology, and azimuth gamma logging technology has proven effective in controlling the drilling of short-radius horizontal sections along the seam and ensuring the coal seam drilling ratio. Two major points can be drawn from this:

The two-dimensional resonance exploration technology detected the development of micro faults in the horizontal section of the drilling, enabling trajectory optimization before drilling. The azimuth gamma logging while drilling technology monitored the current drill bit drilling horizon in real-time, ensuring timely and accurate well trajectory adjustment.

The comprehensive use of these technologies has led to a 20% improvement in the coal seam drilling ratio and a 25% reduction in drilling cycle time in tested short-radius wells in the new exploration and development block-W in Qinshui Basin. This provides technical experience for low-cost exploration and development of CBM in new blocks.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

The financial support by the Found of the National Key Research and Development Program and Key Special Fund Project (No.2018YFC0808202) are gratefully acknowledged.

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X.L. conceived the study and, together with Z.J., Y.W., and H.M. did the literature search, selected the studies. X.L. and H.L. extracted the relevant information. X.L. synthesised the data. J.G. drawed pictures. X.L.and Z.J.wrote the first drafts of the paper.Y.W.and H.M.critically revised successive drafs of the paper. All authors approved the final drafts of the manuscript. X.L. is the guarantor of the study.

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Liu, X., Jiang, Z., Wang, Y. et al. Research on trajectory control technology for L-shaped horizontal exploration wells in coalbed methane. Sci Rep 14 , 11343 (2024). https://doi.org/10.1038/s41598-024-60550-4

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World Leaders in Research-Based User Experience

Promptframes: evolving the wireframe for the age of ai.

May 17, 2024 2024-05-17

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In This Article:

The need for quality placeholder content, introducing promptframes, benefits of using promptframes, how to use promptframes in the design process, the potential of promptframes, a few precautions to consider, a good tactic for the 1-person ux team.

Using placeholder text or images early in your design workflow can help you explore possibilities and cope with evolving requirements.

However, placeholder content (especially the notorious lorem ipsum) can be a barrier to gathering insightful feedback from users and stakeholders. I have personally experienced derailed usability testing sessions because of my placeholders provoked unintentional confusion and doubt in my participants. (And folks like Anna Kaley have previously highlighted the benefits of taking a content-focused approach in early design work.) In UX, remember that the content inspires feedback , not the container .

To enable a more efficient feedback loop, I propose a new design deliverable: the promptframe . Use promptframes to create realistic placeholder content faster using AI.

Promptframes unite the classic UX wireframe with prompt writing for generative AI.

A promptframe is a design deliverable that documents content goals and requirements for generative-AI prompts based on a wireframe’s layout and functionality.

Promptframes organize and document prompts locationally within an existing wireframe. UX designers can create promptframes early in the design process as they begin crafting interfaces to address requirements. Promptframes describe the goals, purpose, requirements, and other details of the content that goes within various design elements, so that AI can readily assist with content ideation and generation.

Wireframes can sometimes create problems for UX designers:

• Reduced ideation . When designers rush ahead to make prototypes with high visual and interactive fidelity, they may spend less time exploring content. Most ideas are poor, and it's usually through evaluating many ideas (or combining several mediocre) that good designs emerge.
• Obscured requirements . Allowing placeholders to linger within designs can hurt the UX designer in the long run. Unknown requirements or technical constraints that the UX designer discovers too late may result in infeasible or misaligned designs that cannot be easily corrected in due time.
• Diminished feedback. Designs with poor content fidelity are too abstract for users to understand. For example, a data-intensive app with nonsensical charts and tables will be incomprehensible to a data-analyst user accustomed to evaluating realistic data. Users may ignore these areas or ask questions about them in testing, consuming precious session time on what you (mistakenly) felt were unimportant details.

Promptframes address these issues in several ways:

• Efficient ideation . One of the superpowers of generative AI is providing multiple variations of an idea with minimal effort. Promptframes integrate this idea engine into the UX design workflow.
• Improved content fidelity . Specific, focused AI prompts can result in helpful content that, while not necessarily ideal for release, may be good enough for user testing and gathering feedback.
• Faster iteration . Writing prompts may initially require some upfront effort, but that effort is repaid with the ease of incorporating insights from testing and feedback. Content can be pivoted and improved rapidly by sharing those details in subsequent prompts.
• Better collaboration . Visuals are a great help when collaborating, as they build common ground with your team. Yet squiggly lines and lorem ipsum are often too abstract for nondesigners. AI-generated content, as well as the prompts generating it, can stimulate dialog and feedback from colleagues and may surface obscure requirements earlier in the design process.
• Greater focus on objectives . Promptframes ask UX designers go beyond interface components and describe business and user goals. If the UX designer struggles to explain these goals to a generative AI tool, it calls into question the content's purpose.

Conduct your early-stage UX design process normally using sketches and simple wireframes . This work will serve as the foundation for your promptframes once it is digitized in your design tool.

To illustrate promptframes in the design process, we will use hypothetical examples based on a page from Blue Apron's website describing a special promotional offer for people in community-service roles.

1. Establish Context and Describe Users

Documenting and sharing context with the generative AI will improve its ability to assist with content creation. ChatGPT is particularly well suited for promptframes due to its support for various output types.

Consider including these important high-level details in your prompts.

Generative AI also needs user insights to be effective. Share written content from high-quality personas or archetypes that mention user needs, behaviors, goals, pain points, as well as motivations for the product, service, or feature being designed.

All this specificity will give you better results than just using off-the-shelf AI agents that proclaim to fulfill similar content-generation roles. Although this looks like a lot of effort to write or compile, you need to do this only once and can reuse them throughout this project or others.

Remember to leverage AI-tool features that maintain this context. For example, ChatGPT offers a custom - GPT feature that conveniently persists these details. Other AI tools like Gemini or Claude currently don’t support easy reuse of context; for those tools, you will need to capture these details (perhaps in a text document) and feed them into your prompt before discussing project specifics.

2. Write and Document Prompts

With the context and users established with our AI tool, the next step is to document prompts that will direct the AI in content creation. Start by writing down the purpose of the various areas and elements in your design that will contain content.

Always include these details in your prompts:

• Objectives : Why is this piece of content present in the design? How does it benefit the business and the users? User stories and other requirements from a product-manager colleague can be an excellent reference here.
• Desired outcomes : What do you hope users will do or think because of this content?
• Examples : If available, include examples that could serve as inspiration when generating the content.

Here are additional aspects to consider for specific types of content:

• Message : What core message are you trying to convey in this copy? What facts and details must be included?
• Container : Where will the copy be seen (landing page, call-to-action button, error message, etc.)?
• Constraints : Are there word-count limits or other limitations required by the container?
• Tone of voice (conditional) : Should the default tone of voice be adjusted for this copy? For example, softening a typically humorous tone of voice for an error message likely to disappoint the user.
• Subject : Who or what elements should be depicted in the image?
• Actions : Are any actions happening with the subjects in the image?
• Background : Is the background relevant, or should it be plain for easy removal?
• Dimensions : What size should the image be to fit the interface? For example, if real images will be coming from another system, then this would be an excellent opportunity to start asking colleagues about expected dimensions of those real images and documenting that constraint in the prompt.
• Style : How should the image be presented? What illustrative techniques are being used, or should it be a photo?

Some generative AI systems are capable of photorealistic content, but some vendors prohibit its creation as a precaution against abuse and misinformation. Don't waste time trying to work around these prohibitions if your current AI tool won't comply. You may need to use a different AI tool or settle for less than true-to-life images.

Data Visualizations

• Type : Describe the specific visualization desired, such as a bar chart, line chart, or table.
• Data and outliers : Provide a spreadsheet of data or request AI to create synthetic data to illustrate a desirable visualization. For example, instead of handcrafting data, just describe that a specific product line should trend downward over time on a line chart if a downward trend would support a task in future usability testing.
• Columns and totals : Where applicable, describe table-column labels, desired totals, and reasonable upper and lower values. Again, consider what might be helpful to represent in future usability-testing tasks.
• Sorting : For tables, describe any default sorting of the data.
• Axes : Describe the components of chart axes, such as minimum and maximum values, data type, and label formatting.
• Style : Provide a color palette for charting elements, if relevant.
• Background : Describe the background fill and any usage of reference lines.
• Legend : Describe the content and placement of a legend, if relevant and desired.
• Labels : Consider data labels for specific data points or the label of the overall chart.
• Dimensions : For charts, describe what size and image format should be used.

3. Run Prompts in AI Tool and Populate Your Designs with Content

Copy and paste the prompts into your AI tool. Then integrate the generated content in the wireframe to start evolving it into a prototype. To keep your work organized, document links to separate AI-tool chats in the promptframe, as you will likely revisit them in future revisions.

When performing this step:

• Guard against perfectionism . Don’t be tempted to create production-ready content. You can inadvertently waste a lot of time trying to refine the AI tool’s output to be “just right” for only marginal improvements.
• Chunk your prompts . AI tools have token limits for prompts and the AI tool’s input and resulting output. For ChatGPT, that limit currently translates to about 2,000-2,500 words. You still need detailed prompts to be successful, though, so break very long prompts into chunks and run them separately so the AI tool can still provide a detailed response.

4. Refine Through Collaboration and Testing

As you conduct design critiques with your collaborators, review the AI-generated content or the prompts that were used. Parts may be added, revised, or dropped — which is normal — but you should always be progressing towards greater content fidelity in all aspects of the design.

Think of the AI-generated content as a provocation for your colleagues — is this content aligned with our project and user's goals? Why or why not? Capture that feedback by revising the prompts. If there's considerable disagreement, consider splitting the design into 2 prototypes for testing.

Remember, promptframing aims to quickly construct a testable design with meaningful content . Consider the tasks you want participants to perform with the proposed design and use them to influence your prompt writing and content-creation strategy.

5. Iterate Quickly

Following this process should buy you more time, and skilled UX professionals know to reinvest those time dividends into iteration. Revise your prompts with your research insights and regenerate new content for future testing. Weaker parts should be scrapped or have their prompt revised before rerunning it in the generative AI tool.

6. Craft Quality Content

Once you have finished iteration, give your successful prototype the "human white-glove treatment" and elevate it more content, visual, or interactive fidelity. Human effort will still be required to create the final design! However, you should have received a higher volume of richer feedback covering more design ideas, resulting in an overall more effective design. You can even share your prompts with other human collaborators to give them additional context on the prototype.

UX-design tools are currently exploring generative AI. Some vendors make bold claims, but their practical utility to UX professionals is not so bold (see our review of the current state of AI tools for UX design .) These tools may someday output robust experiences with basic prompting, but what's likely to happen currently is a mishmash of incoherent material derived from commonplace design patterns needing an excessive amount of rework to be useful.  Whether machine or human — garbage in is garbage out.

Promptframes acknowledge that current generative-AI technology can be practical and helpful in the UX-design workflow. But they nudge us to chunk content challenges into well-documented pieces and don't excuse us from thinking and deciding what is needed and why from a user perspective. Instead, they accelerate our ability to check our assumptions with content that users can meaningfully evaluate and give us feedback on.

Perhaps future UX-design tools will offer better support for documenting prompt inputs and their associated generated outputs to help designers create and refine promptframes efficiently within their project's context. Passing a designer’s prompt via an API call to a generative AI platform is simply not enough.

No single UX deliverable can do it all. There are a few precautions to consider specifically with promptframes.

Not for Executive Consumption

Promptframes, like their wireframe cousins, are not suitable for reviews with executives. People cognitively distant from a project typically need high visual and content fidelity to understand design deliverables. At a minimum, promptframes can convey some forward progress (you've been hard at work making something for this project) but don't expect early-stage promptframes to be particularly helpful in a design review with stakeholders deciding the project's direction or future investment.

Content Will Require Revision

Depending on the details provided in the prompts and the generative AI's robustness, the resulting AI-generated content will vary wildly in quality. Images may be inconsistently styled, and copy will undoubtedly need editing. Remember, the goal is not pixel-perfect, launch-ready content but to have sensible content faster so colleagues or testing participants might reasonably understand and share insightful feedback.

Respect Organizational AI Policies

Some organizations regulate the use of generative AI tools to protect their data. Be aware of and adhere to these before using promptframes.

Many UX professionals are a 1-person UX team or work in environments with low UX maturity , with few resources or specialized collaborators. These folks benefit from augmenting their workflow to accommodate an AI content assistant, particularly if writing or graphic design are not strong skills.

However, what if you can collaborate with a content strategist or UX writer? That’s wonderful! Think of promptframes as a collaborative deliverable with these roles, which are (unfortunately) often included very late in the design process. Use the same general workflow described above to get their feedback and suggestions into the design early so their contributions can be tested along with yours.

Promptframes combine our thinking of content containers with a greater emphasis on the content itself in a way that enables generative AI to accelerate our workflow for user testing and feedback. Lorem ipsum as a placeholder practice is as dead as Latin is as a spoken language. Leave Cicero to the philosophers and use promptframes to rapidly create content your users can understand.

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2024 Ultimate Guide to Website Redesigning: A Step-by-Step Process

In today's fast-paced digital landscape, website redesigning has become crucial to maintaining a solid online presence. As technology and user preferences evolve, businesses must update their websites to stay competitive and relevant. Whether enhancing user experience, improving performance, or aligning with current design trends, website redesigning is pivotal in driving business growth and success.

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A well-executed website redesign can significantly impact a company's brand image, credibility, and online visibility. It provides an opportunity to revamp outdated features, enhance functionality, and incorporate the latest design elements that resonate with the target audience. Moreover, an updated website can help boost search engine rankings and attract more organic traffic, leading to higher conversion rates and increased revenue.

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Key Factors to Consider Before Redesigning

Before embarking on a website redesign project, it's crucial to consider several key factors that can influence its success. Factors such as setting clear goals, understanding target audience needs, and evaluating current website performance metrics are essential for creating an effective strategy that aligns with business objectives. By addressing these factors proactively, businesses can minimize potential risks and maximize the benefits of their website redesign efforts.

Planning for Redesign

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Analyzing Current Website Performance

To start the redesign process, deeply dive into your website's analytics to gain insights into user behavior and engagement. Look for pages with high bounce rates or low conversion rates, as these areas may need attention during the redesign. Understanding how visitors interact with your current site will help you make informed decisions about what changes must be made.

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Creating a Realistic Redesign Timeline

A realistic timeline is essential for managing expectations and ensuring the redesign stays on track. When creating your timeline, consider content creation, design mockups, development work, and testing phases. Setting achievable milestones and deadlines allows the project to move forward smoothly while allowing ample time for thorough testing and revisions.

By carefully planning each step of the website redesign process, you can ensure that your new website aligns with your business goals and effectively engages users to increase conversion rates. This involves analyzing your current website's performance, setting clear redesign goals, and creating a realistic timeline for the redesign. By conducting thorough research and analysis of your target audience and competitors, you can make informed decisions about the design and development of your new website to optimize user experience. Additionally, implementing a strong content strategy and conducting thorough testing before launch will set the stage for post-launch optimization to maximize success.

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When it comes to website redesigning, conducting a competitive analysis is crucial to understanding your industry's current trends and best practices. By analyzing your competitors' websites, you can gain valuable insights into what works and what doesn't, helping you make informed decisions about your redesign. Understanding user behavior through analytics is another key aspect of the research phase. By delving into data such as page views, bounce rates, and conversion rates, you can pinpoint areas of improvement and tailor your redesign to better meet user needs.

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Utilizing tools like Google Analytics allows you to track user interactions on your current website, providing valuable insights into which pages are performing well and which ones need improvement. This data-driven approach will guide your decisions during the redesign process, ensuring that the new website meets user expectations and improves overall engagement.

Identifying Target Audience and Their Needs

Understanding your target audience's demographics, preferences, and pain points is essential for a successful website redesign. By creating user personas based on thorough research and analysis, you can tailor the new website to address specific audience needs and deliver a more personalized experience.

Design and Development

Implementing user-friendly navigation is crucial for enhancing the overall user experience when a website is redesigned. By organizing content clearly and intuitively, visitors can easily find what they want, leading to increased engagement and conversion rates. A well-structured navigation system also helps search engines crawl and index your site more effectively, boosting your SEO efforts.

Implementing User-Friendly Navigation

To make your website more user-friendly, think about streamlining your menu options and using clear, descriptive labels. You can also add dropdown menus to make it easier for visitors to find subpages. Another helpful feature is a search bar, which allows users to quickly locate specific information. These simple adjustments can greatly improve the overall user experience on your website.

Optimizing for Mobile Responsiveness

In today's digital landscape, mobile responsiveness is non-negotiable when it comes to website redesigning. With the majority of internet traffic coming from mobile devices, ensuring that your website looks and functions seamlessly across various screen sizes is essential for reaching a wider audience and delivering a consistent user experience.

Incorporating Engaging Visual Elements

Visual elements such as high-quality images, videos, infographics, and interactive features capture visitors' attention and convey information effectively. When redesigning your website, focus on incorporating visually appealing content that aligns with your brand identity and enhances the overall aesthetics of your site.

Content Strategy

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Content strategy plays a crucial role in attracting and engaging visitors during website redesigning. Auditing existing content is essential to identifying what works and what needs improvement. By conducting a thorough audit, you can ensure that your new website content aligns with your goals and resonates with your target audience.

Auditing Existing Content

During the audit, it's essential to assess the performance of your current content in terms of engagement, conversion rates, and search engine visibility. This will help you identify areas that need improvement and determine which pieces of content can be repurposed or updated to better align with your website redesign goals.

Creating Fresh and SEO-Optimized Content

When creating fresh content for your redesigned website, focusing on search engine optimization (SEO) to improve visibility and attract organic traffic is crucial. You can enhance your website's ranking on search engine results pages by incorporating relevant keywords, optimizing meta tags, and creating valuable, informative content.

Implementing Effective Call-to-Actions

Effective call-to-actions (CTAs) are essential for guiding visitors through the conversion funnel on your redesigned website. Whether it's prompting them to sign up for a newsletter, download a resource, or purchase, strategically placed CTAs can significantly impact user engagement and conversion rates.

Testing and Launch

Thorough quality assurance ensures the new website functions seamlessly. This involves testing all aspects of the website, including functionality, usability, and performance, to identify and fix any issues before launch. Extensive testing on different devices and browsers is essential to ensure a consistent user experience.

Conducting Thorough Quality Assurance

Before launching the redesigned website, comprehensive quality assurance testing is essential to identify any bugs or issues that may affect user experience. This involves checking all links, forms, and interactive elements to ensure they function as intended. Testing for mobile responsiveness and cross-browser compatibility is crucial to provide a seamless experience across different devices.

Ensuring Seamless Integration of Tools and Plugins

As part of the website redesign strategy, it is essential to ensure that all third-party tools and plugins are seamlessly integrated into the new website. This includes ensuring that analytics tools, contact forms, social media integrations, and other plugins are correctly configured and functioning. Any outdated or redundant tools should be removed during this process.

Implementing a Smooth Launch Plan

It's important to have a well-defined launch plan in place to ensure a successful launch of the redesigned website. This includes setting up redirects for any changed URLs, notifying search engines of the website changes through sitemaps or indexing requests, and coordinating with marketing teams for promotional activities. A smooth launch plan helps minimize downtime and ensures a seamless user transition.

Now you understand what goes into the testing and launch phase of website redesigning. Conducting thorough quality assurance, ensuring seamless integration of tools and plugins, and implementing a smooth launch plan are essential steps in achieving success with your redesigned website. By following these tips as part of your guide to website redesigning, you can maximize user engagement and conversion rates while leveraging Strikingly features for an effective redesign.

Post-Launch Optimization

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After your redesigned website launches successfully, it's crucial to focus on post-launch optimization to ensure continued success. Monitoring website performance is key to identifying areas that may need improvement or adjustment. This involves tracking metrics such as traffic, bounce rates, and conversion rates to gauge the effectiveness of the redesign.

Gathering user feedback is another essential aspect of post-launch optimization. By actively seeking input from visitors and customers, you can gain valuable insights into their experience with the new website design . This feedback can help identify pain points, preferences, and areas for improvement that may not be immediately apparent from analytics data.

Continuous improvements based on monitoring and user feedback are vital for the long-term success of your website redesign. Whether it's tweaking navigation elements for better usability or refining content to resonate with your target audience, ongoing refinement is vital to keeping your website fresh and effective.

Now that we've discussed the significance of post-launch optimization in website redesigning, it's time to wrap up with our conclusion. In this final section, we'll summarize how implementing these strategies can lead to greater user engagement and improved conversion rates. By leveraging the features offered by Strikingly for an effective redesign, you can ensure that your website not only looks great but also performs exceptionally well in driving business results.

Achieve Success with Website Redesigning

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Website redesigning is a crucial process that can significantly impact your online success. By following the right website redesign tips and strategy, you can achieve success with your website overhaul. Maximizing user engagement and conversion rates should be the ultimate goal of any website redesign project. Leveraging Strikingly features for effective redesign can help you create a visually appealing and functional website that resonates with your target audience.

Achieving success with website redesign involves careful planning, thorough research, and seamless execution. It's important to set clear goals for the redesign project and analyze user behavior to understand what will engage them. Creating engaging content that resonates with the target audience is also crucial for a successful website redesign. By focusing on these key elements, businesses can ensure that their redesigned website achieves its intended objectives and delivers a positive user experience.

Maximizing User Engagement and Conversion Rates

Maximizing user engagement and conversion rates should be at the forefront of your website redesign strategy. Implementing user-friendly navigation, optimizing mobile responsiveness, and incorporating engaging visual elements are essential in driving user interaction and boosting conversion rates.

Leveraging Strikingly Features for Effective Redesign

Leveraging Strikingly features for effective redesign can streamline the entire process of revamping your website. From customizable templates to powerful integrations, Strikingly offers a range of tools to help you create a stunning and high-performing webpage that aligns with your brand's vision.

Redesigning your website is more than just making it look pretty; it's about giving your visitors a seamless and engaging online experience. By following these strategies and using the right tools, you can elevate your online presence with a successful website redesign project. So, don't just focus on the aesthetics, but also prioritize creating an intuitive and compelling digital experience for your visitors to ensure that they keep coming back for more.

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