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Methodology

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.

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

Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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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.

Organizing Your Social Sciences Research Paper

Types of Research Designs
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
Review and synthesize previously published literature associated with the research problem,
Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

This is a collaborative and adaptive research design that lends itself to use in work or community situations.
Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
Action research studies often have direct and obvious relevance to improving practice and advocating for change.
There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
Personal over-involvement of the researcher may bias research results.
The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA: Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
Design can extend experience or add strength to what is already known through previous research.
Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
The design can provide detailed descriptions of specific and rare cases.
A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
Design does not facilitate assessment of cause and effect relationships.
Vital information may be missing, making the case hard to interpret.
The case may not be representative or typical of the larger problem being investigated.
If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
Replication is possible.
There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
Either original data or secondary data can be used in this design.
In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
Studies cannot be utilized to establish cause and effect relationships.
This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
If the limitations are understood, they can be a useful tool in developing a more focused study.
Descriptive studies can yield rich data that lead to important recommendations in practice.
Appoach collects a large amount of data for detailed analysis.
The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
Approach provides the highest level of evidence for single studies.
The design is artificial, and results may not generalize well to the real world.
The artificial settings of experiments may alter the behaviors or responses of participants.
Experimental designs can be costly if special equipment or facilities are needed.
Some research problems cannot be studied using an experiment because of ethical or technical reasons.
Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

Familiarity with basic details, settings, and concerns.
Well grounded picture of the situation being developed.
Generation of new ideas and assumptions.
Development of tentative theories or hypotheses.
Determination about whether a study is feasible in the future.
Issues get refined for more systematic investigation and formulation of new research questions.
Direction for future research and techniques get developed.
Design is a useful approach for gaining background information on a particular topic.
Exploratory research is flexible and can address research questions of all types (what, why, how).
Provides an opportunity to define new terms and clarify existing concepts.
Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field [i.e., the act of triangulating the data].
Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

The historical research design is unobtrusive; the act of research does not affect the results of the study.
The historical approach is well suited for trend analysis.
Historical records can add important contextual background required to more fully understand and interpret a research problem.
There is often no possibility of researcher-subject interaction that could affect the findings.
Historical sources can be used over and over to study different research problems or to replicate a previous study.
The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
Interpreting historical sources can be very time consuming.
The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58; Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
The data collection method may change over time.
Maintaining the integrity of the original sample can be difficult over an extended period of time.
It can be difficult to show more than one variable at a time.
This design often needs qualitative research data to explain fluctuations in the results.
A longitudinal research design assumes present trends will continue unchanged.
It can take a long period of time to gather results.
There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
A well-reasoned and well-documented justification for identification and selection of the studies;
Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
Justification of the techniques used to evaluate the studies.
Can be an effective strategy for determining gaps in the literature.
Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
Can be used to generate new hypotheses or highlight research problems for future studies.
Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
A large sample size can yield reliable, but not necessarily valid, results.
A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
The strengths of one method can be used to overcome the inherent weaknesses of another method.
Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
The researcher is able to collect in-depth information about a particular behavior.
Can reveal interrelationships among multifaceted dimensions of group interactions.
You can generalize your results to real life situations.
Observational research is useful for discovering what variables may be important before applying other methods like experiments.
Observation research designs account for the complexity of group behaviors.
Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
There can be problems with bias as the researcher may only "see what they want to see."
There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
Sources or subjects may not all be equally credible.
Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
Can provide a basis for applying ethical decision-making to practice.
Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
Brings clarity to general guiding practices and principles of an individual or group.
Philosophy informs methodology.
Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
Limited application to specific research problems [answering the "So What?" question in social science research].
Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
There are limitations in the use of metaphor as a vehicle of philosophical analysis.
There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

The researcher has a limitless option when it comes to sample size and the sampling schedule.
Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
This is a useful design for exploratory studies.
There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods . David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research." Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Pediatr Investig
v.3(4); 2019 Dec

Clinical research study designs: The essentials

Ambika g. chidambaram.

1 Children's Hospital of Philadelphia, Philadelphia Pennsylvania, USA

Maureen Josephson

In clinical research, our aim is to design a study which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research study that rests on a strong foundation of a detailed methodology and governed by ethical clinical principles. The purpose of this review is to provide the readers an overview of the basic study designs and its applicability in clinical research.

Introduction

In clinical research, our aim is to design a study, which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods that can be translated to the “real world” setting. 1 Before choosing a study design, one must establish aims and objectives of the study, and choose an appropriate target population that is most representative of the population being studied. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research study that rests on a strong foundation of a detailed methodology and is governed by ethical principles. 2

From an epidemiological standpoint, there are two major types of clinical study designs, observational and experimental. 3 Observational studies are hypothesis‐generating studies, and they can be further divided into descriptive and analytic. Descriptive observational studies provide a description of the exposure and/or the outcome, and analytic observational studies provide a measurement of the association between the exposure and the outcome. Experimental studies, on the other hand, are hypothesis testing studies. It involves an intervention that tests the association between the exposure and outcome. Each study design is different, and so it would be important to choose a design that would most appropriately answer the question in mind and provide the most valuable information. We will be reviewing each study design in detail (Figure 1 ).

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Overview of clinical research study designs

Observational study designs

Observational studies ask the following questions: what, who, where and when. There are many study designs that fall under the umbrella of descriptive study designs, and they include, case reports, case series, ecologic study, cross‐sectional study, cohort study and case‐control study (Figure 2 ).

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Classification of observational study designs

Case reports and case series

Every now and then during clinical practice, we come across a case that is atypical or ‘out of the norm’ type of clinical presentation. This atypical presentation is usually described as case reports which provides a detailed and comprehensive description of the case. 4 It is one of the earliest forms of research and provides an opportunity for the investigator to describe the observations that make a case unique. There are no inferences obtained and therefore cannot be generalized to the population which is a limitation. Most often than not, a series of case reports make a case series which is an atypical presentation found in a group of patients. This in turn poses the question for a new disease entity and further queries the investigator to look into mechanistic investigative opportunities to further explore. However, in a case series, the cases are not compared to subjects without the manifestations and therefore it cannot determine which factors in the description are unique to the new disease entity.

Ecologic study

Ecological studies are observational studies that provide a description of population group characteristics. That is, it describes characteristics to all individuals within a group. For example, Prentice et al 5 measured incidence of breast cancer and per capita intake of dietary fat, and found a correlation that higher per capita intake of dietary fat was associated with an increased incidence of breast cancer. But the study does not conclude specifically which subjects with breast cancer had a higher dietary intake of fat. Thus, one of the limitations with ecologic study designs is that the characteristics are attributed to the whole group and so the individual characteristics are unknown.

Cross‐sectional study

Cross‐sectional studies are study designs used to evaluate an association between an exposure and outcome at the same time. It can be classified under either descriptive or analytic, and therefore depends on the question being answered by the investigator. Since, cross‐sectional studies are designed to collect information at the same point of time, this provides an opportunity to measure prevalence of the exposure or the outcome. For example, a cross‐sectional study design was adopted to estimate the global need for palliative care for children based on representative sample of countries from all regions of the world and all World Bank income groups. 6 The limitation of cross‐sectional study design is that temporal association cannot be established as the information is collected at the same point of time. If a study involves a questionnaire, then the investigator can ask questions to onset of symptoms or risk factors in relation to onset of disease. This would help in obtaining a temporal sequence between the exposure and outcome. 7

Case‐control study

Case‐control studies are study designs that compare two groups, such as the subjects with disease (cases) to the subjects without disease (controls), and to look for differences in risk factors. 8 This study is used to study risk factors or etiologies for a disease, especially if the disease is rare. Thus, case‐control studies can also be hypothesis testing studies and therefore can suggest a causal relationship but cannot prove. It is less expensive and less time‐consuming than cohort studies (described in section “Cohort study”). An example of a case‐control study was performed in Pakistan evaluating the risk factors for neonatal tetanus. They retrospectively reviewed a defined cohort for cases with and without neonatal tetanus. 9 They found a strong association of the application of ghee (clarified butter) as a risk factor for neonatal tetanus. Although this suggests a causal relationship, cause cannot be proven by this methodology (Figure 3 ).

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Case‐control study design

One of the limitations of case‐control studies is that they cannot estimate prevalence of a disease accurately as a proportion of cases and controls are studied at a time. Case‐control studies are also prone to biases such as recall bias, as the subjects are providing information based on their memory. Hence, the subjects with disease are likely to remember the presence of risk factors compared to the subjects without disease.

One of the aspects that is often overlooked is the selection of cases and controls. It is important to select the cases and controls appropriately to obtain a meaningful and scientifically sound conclusion and this can be achieved by implementing matching. Matching is defined by Gordis et al as ‘the process of selecting the controls so that they are similar to the cases in certain characteristics such as age, race, sex, socioeconomic status and occupation’ 7 This would help identify risk factors or probable etiologies that are not due to differences between the cases and controls.

Cohort study

Cohort studies are study designs that compare two groups, such as the subjects with exposure/risk factor to the subjects without exposure/risk factor, for differences in incidence of outcome/disease. Most often, cohort study designs are used to study outcome(s) from a single exposure/risk factor. Thus, cohort studies can also be hypothesis testing studies and can infer and interpret a causal relationship between an exposure and a proposed outcome, but cannot establish it (Figure 4 ).

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Cohort study design

Cohort studies can be classified as prospective and retrospective. 7 Prospective cohort studies follow subjects from presence of risk factors/exposure to development of disease/outcome. This could take up to years before development of disease/outcome, and therefore is time consuming and expensive. On the other hand, retrospective cohort studies identify a population with and without the risk factor/exposure based on past records and then assess if they had developed the disease/outcome at the time of study. Thus, the study design for prospective and retrospective cohort studies are similar as we are comparing populations with and without exposure/risk factor to development of outcome/disease.

Cohort studies are typically chosen as a study design when the suspected exposure is known and rare, and the incidence of disease/outcome in the exposure group is suspected to be high. The choice between prospective and retrospective cohort study design would depend on the accuracy and reliability of the past records regarding the exposure/risk factor.

Some of the biases observed with cohort studies include selection bias and information bias. Some individuals who have the exposure may refuse to participate in the study or would be lost to follow‐up, and in those instances, it becomes difficult to interpret the association between an exposure and outcome. Also, if the information is inaccurate when past records are used to evaluate for exposure status, then again, the association between the exposure and outcome becomes difficult to interpret.

Case‐control studies based within a defined cohort

Case‐control studies based within a defined cohort is a form of study design that combines some of the features of a cohort study design and a case‐control study design. When a defined cohort is embedded in a case‐control study design, all the baseline information collected before the onset of disease like interviews, surveys, blood or urine specimens, then the cohort is followed onset of disease. One of the advantages of following the above design is that it eliminates recall bias as the information regarding risk factors is collected before onset of disease. Case‐control studies based within a defined cohort can be further classified into two types: Nested case‐control study and Case‐cohort study.

Nested case‐control study

A nested case‐control study consists of defining a cohort with suspected risk factors and assigning a control within a cohort to the subject who develops the disease. 10 Over a period, cases and controls are identified and followed as per the investigator's protocol. Hence, the case and control are matched on calendar time and length of follow‐up. When this study design is implemented, it is possible for the control that was selected early in the study to develop the disease and become a case in the latter part of the study.

Case‐cohort Study

A case‐cohort study is similar to a nested case‐control study except that there is a defined sub‐cohort which forms the groups of individuals without the disease (control), and the cases are not matched on calendar time or length of follow‐up with the control. 11 With these modifications, it is possible to compare different disease groups with the same sub‐cohort group of controls and eliminates matching between the case and control. However, these differences will need to be accounted during analysis of results.

Experimental study design

The basic concept of experimental study design is to study the effect of an intervention. In this study design, the risk factor/exposure of interest/treatment is controlled by the investigator. Therefore, these are hypothesis testing studies and can provide the most convincing demonstration of evidence for causality. As a result, the design of the study requires meticulous planning and resources to provide an accurate result.

The experimental study design can be classified into 2 groups, that is, controlled (with comparison) and uncontrolled (without comparison). 1 In the group without controls, the outcome is directly attributed to the treatment received in one group. This fails to prove if the outcome was truly due to the intervention implemented or due to chance. This can be avoided if a controlled study design is chosen which includes a group that does not receive the intervention (control group) and a group that receives the intervention (intervention/experiment group), and therefore provide a more accurate and valid conclusion.

Experimental study designs can be divided into 3 broad categories: clinical trial, community trial, field trial. The specifics of each study design are explained below (Figure 5 ).

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Experimental study designs

Clinical trial

Clinical trials are also known as therapeutic trials, which involve subjects with disease and are placed in different treatment groups. It is considered a gold standard approach for epidemiological research. One of the earliest clinical trial studies was performed by James Lind et al in 1747 on sailors with scurvy. 12 Lind divided twelve scorbutic sailors into six groups of two. Each group received the same diet, in addition to a quart of cider (group 1), twenty‐five drops of elixir of vitriol which is sulfuric acid (group 2), two spoonfuls of vinegar (group 3), half a pint of seawater (group 4), two oranges and one lemon (group 5), and a spicy paste plus a drink of barley water (group 6). The group who ate two oranges and one lemon had shown the most sudden and visible clinical effects and were taken back at the end of 6 days as being fit for duty. During Lind's time, this was not accepted but was shown to have similar results when repeated 47 years later in an entire fleet of ships. Based on the above results, in 1795 lemon juice was made a required part of the diet of sailors. Thus, clinical trials can be used to evaluate new therapies, such as new drug or new indication, new drug combination, new surgical procedure or device, new dosing schedule or mode of administration, or a new prevention therapy.

While designing a clinical trial, it is important to select the population that is best representative of the general population. Therefore, the results obtained from the study can be generalized to the population from which the sample population was selected. It is also as important to select appropriate endpoints while designing a trial. Endpoints need to be well‐defined, reproducible, clinically relevant and achievable. The types of endpoints include continuous, ordinal, rates and time‐to‐event, and it is typically classified as primary, secondary or tertiary. 2 An ideal endpoint is a purely clinical outcome, for example, cure/survival, and thus, the clinical trials will become very long and expensive trials. Therefore, surrogate endpoints are used that are biologically related to the ideal endpoint. Surrogate endpoints need to be reproducible, easily measured, related to the clinical outcome, affected by treatment and occurring earlier than clinical outcome. 2

Clinical trials are further divided into randomized clinical trial, non‐randomized clinical trial, cross‐over clinical trial and factorial clinical trial.

Randomized clinical trial

A randomized clinical trial is also known as parallel group randomized trials or randomized controlled trials. Randomized clinical trials involve randomizing subjects with similar characteristics to two groups (or multiple groups): the group that receives the intervention/experimental therapy and the other group that received the placebo (or standard of care). 13 This is typically performed by using a computer software, manually or by other methods. Hence, we can measure the outcomes and efficacy of the intervention/experimental therapy being studied without bias as subjects have been randomized to their respective groups with similar baseline characteristics. This type of study design is considered gold standard for epidemiological research. However, this study design is generally not applicable to rare and serious disease process as it would unethical to treat that group with a placebo. Please see section “Randomization” for detailed explanation regarding randomization and placebo.

Non‐randomized clinical trial

A non‐randomized clinical trial involves an approach to selecting controls without randomization. With this type of study design a pattern is usually adopted, such as, selection of subjects and controls on certain days of the week. Depending on the approach adopted, the selection of subjects becomes predictable and therefore, there is bias with regards to selection of subjects and controls that would question the validity of the results obtained.

Historically controlled studies can be considered as a subtype of non‐randomized clinical trial. In this study design subtype, the source of controls is usually adopted from the past, such as from medical records and published literature. 1 The advantages of this study design include being cost‐effective, time saving and easily accessible. However, since this design depends on already collected data from different sources, the information obtained may not be accurate, reliable, lack uniformity and/or completeness as well. Though historically controlled studies maybe easier to conduct, the disadvantages will need to be taken into account while designing a study.

Cross‐over clinical trial

In cross‐over clinical trial study design, there are two groups who undergoes the same intervention/experiment at different time periods of the study. That is, each group serves as a control while the other group is undergoing the intervention/experiment. 14 Depending on the intervention/experiment, a ‘washout’ period is recommended. This would help eliminate residuals effects of the intervention/experiment when the experiment group transitions to be the control group. Hence, the outcomes of the intervention/experiment will need to be reversible as this type of study design would not be possible if the subject is undergoing a surgical procedure.

Factorial trial

A factorial trial study design is adopted when the researcher wishes to test two different drugs with independent effects on the same population. Typically, the population is divided into 4 groups, the first with drug A, the second with drug B, the third with drug A and B, and the fourth with neither drug A nor drug B. The outcomes for drug A are compared to those on drug A, drug A and B and to those who were on drug B and neither drug A nor drug B. 15 The advantages of this study design that it saves time and helps to study two different drugs on the same study population at the same time. However, this study design would not be applicable if either of the drugs or interventions overlaps with each other on modes of action or effects, as the results obtained would not attribute to a particular drug or intervention.

Community trial

Community trials are also known as cluster‐randomized trials, involve groups of individuals with and without disease who are assigned to different intervention/experiment groups. Hence, groups of individuals from a certain area, such as a town or city, or a certain group such as school or college, will undergo the same intervention/experiment. 16 Hence, the results will be obtained at a larger scale; however, will not be able to account for inter‐individual and intra‐individual variability.

Field trial

Field trials are also known as preventive or prophylactic trials, and the subjects without the disease are placed in different preventive intervention groups. 16 One of the hypothetical examples for a field trial would be to randomly assign to groups of a healthy population and to provide an intervention to a group such as a vitamin and following through to measure certain outcomes. Hence, the subjects are monitored over a period of time for occurrence of a particular disease process.

Overview of methodologies used within a study design

Randomization.

Randomization is a well‐established methodology adopted in research to prevent bias due to subject selection, which may impact the result of the intervention/experiment being studied. It is one of the fundamental principles of an experimental study designs and ensures scientific validity. It provides a way to avoid predicting which subjects are assigned to a certain group and therefore, prevent bias on the final results due to subject selection. This also ensures comparability between groups as most baseline characteristics are similar prior to randomization and therefore helps to interpret the results regarding the intervention/experiment group without bias.

There are various ways to randomize and it can be as simple as a ‘flip of a coin’ to use computer software and statistical methods. To better describe randomization, there are three types of randomization: simple randomization, block randomization and stratified randomization.

Simple randomization

In simple randomization, the subjects are randomly allocated to experiment/intervention groups based on a constant probability. That is, if there are two groups A and B, the subject has a 0.5 probability of being allocated to either group. This can be performed in multiple ways, and one of which being as simple as a ‘flip of a coin’ to using random tables or numbers. 17 The advantage of using this methodology is that it eliminates selection bias. However, the disadvantage with this methodology is that an imbalance in the number allocated to each group as well as the prognostic factors between groups. Hence, it is more challenging in studies with a small sample size.

Block randomization

In block randomization, the subjects of similar characteristics are classified into blocks. The aim of block randomization is to balance the number of subjects allocated to each experiment/intervention group. For example, let's assume that there are four subjects in each block, and two of the four subjects in each block will be randomly allotted to each group. Therefore, there will be two subjects in one group and two subjects in the other group. 17 The disadvantage with this methodology is that there is still a component of predictability in the selection of subjects and the randomization of prognostic factors is not performed. However, it helps to control the balance between the experiment/intervention groups.

Stratified randomization

In stratified randomization, the subjects are defined based on certain strata, which are covariates. 18 For example, prognostic factors like age can be considered as a covariate, and then the specified population can be randomized within each age group related to an experiment/intervention group. The advantage with this methodology is that it enables comparability between experiment/intervention groups and thus makes result analysis more efficient. But, with this methodology the covariates will need to be measured and determined before the randomization process. The sample size will help determine the number of strata that would need to be chosen for a study.

Blinding is a methodology adopted in a study design to intentionally not provide information related to the allocation of the groups to the subject participants, investigators and/or data analysts. 19 The purpose of blinding is to decrease influence associated with the knowledge of being in a particular group on the study result. There are 3 forms of blinding: single‐blinded, double‐blinded and triple‐blinded. 1 In single‐blinded studies, otherwise called as open‐label studies, the subject participants are not revealed which group that they have been allocated to. However, the investigator and data analyst will be aware of the allocation of the groups. In double‐blinded studies, both the study participants and the investigator will be unaware of the group to which they were allocated to. Double‐blinded studies are typically used in clinical trials to test the safety and efficacy of the drugs. In triple‐blinded studies, the subject participants, investigators and data analysts will not be aware of the group allocation. Thus, triple‐blinded studies are more difficult and expensive to design but the results obtained will exclude confounding effects from knowledge of group allocation.

Blinding is especially important in studies where subjective response are considered as outcomes. This is because certain responses can be modified based on the knowledge of the experiment group that they are in. For example, a group allocated in the non‐intervention group may not feel better as they are not getting the treatment, or an investigator may pay more attention to the group receiving treatment, and thereby potentially affecting the final results. However, certain treatments cannot be blinded such as surgeries or if the treatment group requires an assessment of the effect of intervention such as quitting smoking.

Placebo is defined in the Merriam‐Webster dictionary as ‘an inert or innocuous substance used especially in controlled experiments testing the efficacy of another substance (such as drug)’. 20 A placebo is typically used in a clinical research study to evaluate the safety and efficacy of a drug/intervention. This is especially useful if the outcome measured is subjective. In clinical drug trials, a placebo is typically a drug that resembles the drug to be tested in certain characteristics such as color, size, shape and taste, but without the active substance. This helps to measure effects of just taking the drug, such as pain relief, compared to the drug with the active substance. If the effect is positive, for example, improvement in mood/pain, then it is called placebo effect. If the effect is negative, for example, worsening of mood/pain, then it is called nocebo effect. 21

The ethics of placebo‐controlled studies is complex and remains a debate in the medical research community. According to the Declaration of Helsinki on the use of placebo released in October 2013, “The benefits, risks, burdens and effectiveness of a new intervention must be tested against those of the best proven intervention(s), except in the following circumstances:

Where no proven intervention exists, the use of placebo, or no intervention, is acceptable; or

Where for compelling and scientifically sound methodological reasons the use of any intervention less effective than the best proven one, the use of placebo, or no intervention is necessary to determine the efficacy or safety of an intervention and the patients who receive any intervention less effective than the best proven one, placebo, or no intervention will not be subject to additional risks of serious or irreversible harm as a result of not receiving the best proven intervention.

Extreme care must be taken to avoid abuse of this option”. 22

Hence, while designing a research study, both the scientific validity and ethical aspects of the study will need to be thoroughly evaluated.

Bias has been defined as “any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure's effect on the risk of disease”. 23 There are multiple types of biases and so, in this review we will focus on the following types: selection bias, information bias and observer bias. Selection bias is when a systematic error is committed while selecting subjects for the study. Selection bias will affect the external validity of the study if the study subjects are not representative of the population being studied and therefore, the results of the study will not be generalizable. Selection bias will affect the internal validity of the study if the selection of study subjects in each group is influenced by certain factors, such as, based on the treatment of the group assigned. One of the ways to decrease selection bias is to select the study population that would representative of the population being studied, or to randomize (discussed in section “Randomization”).

Information bias is when a systematic error is committed while obtaining data from the study subjects. This can be in the form of recall bias when subject is required to remember certain events from the past. Typically, subjects with the disease tend to remember certain events compared to subjects without the disease. Observer bias is a systematic error when the study investigator is influenced by the certain characteristics of the group, that is, an investigator may pay closer attention to the group receiving the treatment versus the group not receiving the treatment. This may influence the results of the study. One of the ways to decrease observer bias is to use blinding (discussed in section “Blinding”).

Thus, while designing a study it is important to take measure to limit bias as much as possible so that the scientific validity of the study results is preserved to its maximum.

Overview of drug development in the United States of America

Now that we have reviewed the various clinical designs, clinical trials form a major part in development of a drug. In the United States, the Food and Drug Administration (FDA) plays an important role in getting a drug approved for clinical use. It includes a robust process that involves four different phases before a drug can be made available to the public. Phase I is conducted to determine a safe dose. The study subjects consist of normal volunteers and/or subjects with disease of interest, and the sample size is typically small and not more than 30 subjects. The primary endpoint consists of toxicity and adverse events. Phase II is conducted to evaluate of safety of dose selected in Phase I, to collect preliminary information on efficacy and to determine factors to plan a randomized controlled trial. The study subjects consist of subjects with disease of interest and the sample size is also small but more that Phase I (40–100 subjects). The primary endpoint is the measure of response. Phase III is conducted as a definitive trial to prove efficacy and establish safety of a drug. Phase III studies are randomized controlled trials and depending on the drug being studied, it can be placebo‐controlled, equivalence, superiority or non‐inferiority trials. The study subjects consist of subjects with disease of interest, and the sample size is typically large but no larger than 300 to 3000. Phase IV is performed after a drug is approved by the FDA and it is also called the post‐marketing clinical trial. This phase is conducted to evaluate new indications, to determine safety and efficacy in long‐term follow‐up and new dosing regimens. This phase helps to detect rare adverse events that would not be picked up during phase III studies and decrease in the delay in the release of the drug in the market. Hence, this phase depends heavily on voluntary reporting of side effects and/or adverse events by physicians, non‐physicians or drug companies. 2

We have discussed various clinical research study designs in this comprehensive review. Though there are various designs available, one must consider various ethical aspects of the study. Hence, each study will require thorough review of the protocol by the institutional review board before approval and implementation.

CONFLICT OF INTEREST

Chidambaram AG, Josephson M. Clinical research study designs: The essentials . Pediatr Invest . 2019; 3 :245‐252. 10.1002/ped4.12166 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

What’s Included: Research Paper Template

If you’re preparing to write an academic research paper, our free research paper template is the perfect starting point. In the template, we cover every section step by step, with clear, straightforward explanations and examples .

The template’s structure is based on the tried and trusted best-practice format for formal academic research papers. The template structure reflects the overall research process, ensuring your paper will have a smooth, logical flow from chapter to chapter.

The research paper template covers the following core sections:

The title page/cover page
Abstract (sometimes also called the executive summary)
Section 1: Introduction
Section 2: Literature review
Section 3: Methodology
Section 4: Findings /results
Section 5: Discussion
Section 6: Conclusion
Reference list

Each section is explained in plain, straightforward language , followed by an overview of the key elements that you need to cover within each section. We’ve also included links to free resources to help you understand how to write each section.

The cleanly formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

FAQs: Research Paper Template

What format is the template (doc, pdf, ppt, etc.).

The research paper template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

What types of research papers can this template be used for?

The template follows the standard best-practice structure for formal academic research papers, so it is suitable for the vast majority of degrees, particularly those within the sciences.

Some universities may have some additional requirements, but these are typically minor, with the core structure remaining the same. Therefore, it’s always a good idea to double-check your university’s requirements before you finalise your structure.

Is this template for an undergrad, Masters or PhD-level research paper?

This template can be used for a research paper at any level of study. It may be slight overkill for an undergraduate-level study, but it certainly won’t be missing anything.

How long should my research paper be?

This depends entirely on your university’s specific requirements, so it’s best to check with them. We include generic word count ranges for each section within the template, but these are purely indicative.

What about the research proposal?

If you’re still working on your research proposal, we’ve got a template for that here .

We’ve also got loads of proposal-related guides and videos over on the Grad Coach blog .

How do I write a literature review?

We have a wealth of free resources on the Grad Coach Blog that unpack how to write a literature review from scratch. You can check out the literature review section of the blog here.

How do I create a research methodology?

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Home » Research Paper Format – Types, Examples and Templates

Research Paper Format – Types, Examples and Templates

Table of Contents

Research paper format is an essential aspect of academic writing that plays a crucial role in the communication of research findings . The format of a research paper depends on various factors such as the discipline, style guide, and purpose of the research. It includes guidelines for the structure, citation style, referencing , and other elements of the paper that contribute to its overall presentation and coherence. Adhering to the appropriate research paper format is vital for ensuring that the research is accurately and effectively communicated to the intended audience. In this era of information, it is essential to understand the different research paper formats and their guidelines to communicate research effectively, accurately, and with the required level of detail. This post aims to provide an overview of some of the common research paper formats used in academic writing.

Research Paper Formats

Research Paper Formats are as follows:

APA (American Psychological Association) format
MLA (Modern Language Association) format
Chicago/Turabian style
IEEE (Institute of Electrical and Electronics Engineers) format
AMA (American Medical Association) style
Harvard style
Vancouver style
ACS (American Chemical Society) style
ASA (American Sociological Association) style
APSA (American Political Science Association) style

APA (American Psychological Association) Format

Here is a general APA format for a research paper:

Title Page: The title page should include the title of your paper, your name, and your institutional affiliation. It should also include a running head, which is a shortened version of the title, and a page number in the upper right-hand corner.
Abstract : The abstract is a brief summary of your paper, typically 150-250 words. It should include the purpose of your research, the main findings, and any implications or conclusions that can be drawn.
Introduction: The introduction should provide background information on your topic, state the purpose of your research, and present your research question or hypothesis. It should also include a brief literature review that discusses previous research on your topic.
Methods: The methods section should describe the procedures you used to collect and analyze your data. It should include information on the participants, the materials and instruments used, and the statistical analyses performed.
Results: The results section should present the findings of your research in a clear and concise manner. Use tables and figures to help illustrate your results.
Discussion : The discussion section should interpret your results and relate them back to your research question or hypothesis. It should also discuss the implications of your findings and any limitations of your study.
References : The references section should include a list of all sources cited in your paper. Follow APA formatting guidelines for your citations and references.

Some additional tips for formatting your APA research paper:

Use 12-point Times New Roman font throughout the paper.
Double-space all text, including the references.
Use 1-inch margins on all sides of the page.
Indent the first line of each paragraph by 0.5 inches.
Use a hanging indent for the references (the first line should be flush with the left margin, and all subsequent lines should be indented).
Number all pages, including the title page and references page, in the upper right-hand corner.

APA Research Paper Format Template

APA Research Paper Format Template is as follows:

Title Page:

Title of the paper
Author’s name
Institutional affiliation
A brief summary of the main points of the paper, including the research question, methods, findings, and conclusions. The abstract should be no more than 250 words.

Introduction:

Background information on the topic of the research paper
Research question or hypothesis
Significance of the study
Overview of the research methods and design
Brief summary of the main findings
Participants: description of the sample population, including the number of participants and their characteristics (age, gender, ethnicity, etc.)
Materials: description of any materials used in the study (e.g., survey questions, experimental apparatus)
Procedure: detailed description of the steps taken to conduct the study
Presentation of the findings of the study, including statistical analyses if applicable
Tables and figures may be included to illustrate the results

Discussion:

Interpretation of the results in light of the research question and hypothesis
Implications of the study for the field
Limitations of the study
Suggestions for future research

References:

A list of all sources cited in the paper, in APA format

Formatting guidelines:

Double-spaced
12-point font (Times New Roman or Arial)
1-inch margins on all sides
Page numbers in the top right corner
Headings and subheadings should be used to organize the paper
The first line of each paragraph should be indented
Quotations of 40 or more words should be set off in a block quote with no quotation marks
In-text citations should include the author’s last name and year of publication (e.g., Smith, 2019)

APA Research Paper Format Example

APA Research Paper Format Example is as follows:

The Effects of Social Media on Mental Health

University of XYZ

This study examines the relationship between social media use and mental health among college students. Data was collected through a survey of 500 students at the University of XYZ. Results suggest that social media use is significantly related to symptoms of depression and anxiety, and that the negative effects of social media are greater among frequent users.

Social media has become an increasingly important aspect of modern life, especially among young adults. While social media can have many positive effects, such as connecting people across distances and sharing information, there is growing concern about its impact on mental health. This study aims to examine the relationship between social media use and mental health among college students.

Participants: Participants were 500 college students at the University of XYZ, recruited through online advertisements and flyers posted on campus. Participants ranged in age from 18 to 25, with a mean age of 20.5 years. The sample was 60% female, 40% male, and 5% identified as non-binary or gender non-conforming.

Data was collected through an online survey administered through Qualtrics. The survey consisted of several measures, including the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and questions about social media use.

Procedure :

Participants were asked to complete the online survey at their convenience. The survey took approximately 20-30 minutes to complete. Data was analyzed using descriptive statistics, correlations, and multiple regression analysis.

Results indicated that social media use was significantly related to symptoms of depression (r = .32, p < .001) and anxiety (r = .29, p < .001). Regression analysis indicated that frequency of social media use was a significant predictor of both depression symptoms (β = .24, p < .001) and anxiety symptoms (β = .20, p < .001), even when controlling for age, gender, and other relevant factors.

The results of this study suggest that social media use is associated with symptoms of depression and anxiety among college students. The negative effects of social media are greater among frequent users. These findings have important implications for mental health professionals and educators, who should consider addressing the potential negative effects of social media use in their work with young adults.

References :

References should be listed in alphabetical order according to the author’s last name. For example:

Chou, H. T. G., & Edge, N. (2012). “They are happier and having better lives than I am”: The impact of using Facebook on perceptions of others’ lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117-121.
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3-17.

Note: This is just a sample Example do not use this in your assignment.

MLA (Modern Language Association) Format

MLA (Modern Language Association) Format is as follows:

Page Layout : Use 8.5 x 11-inch white paper, with 1-inch margins on all sides. The font should be 12-point Times New Roman or a similar serif font.
Heading and Title : The first page of your research paper should include a heading and a title. The heading should include your name, your instructor’s name, the course title, and the date. The title should be centered and in title case (capitalizing the first letter of each important word).
In-Text Citations : Use parenthetical citations to indicate the source of your information. The citation should include the author’s last name and the page number(s) of the source. For example: (Smith 23).
Works Cited Page : At the end of your paper, include a Works Cited page that lists all the sources you used in your research. Each entry should include the author’s name, the title of the work, the publication information, and the medium of publication.
Formatting Quotations : Use double quotation marks for short quotations and block quotations for longer quotations. Indent the entire quotation five spaces from the left margin.
Formatting the Body : Use a clear and readable font and double-space your text throughout. The first line of each paragraph should be indented one-half inch from the left margin.

MLA Research Paper Template

MLA Research Paper Format Template is as follows:

Use 8.5 x 11 inch white paper.
Use a 12-point font, such as Times New Roman.
Use double-spacing throughout the entire paper, including the title page and works cited page.
Set the margins to 1 inch on all sides.
Use page numbers in the upper right corner, beginning with the first page of text.
Include a centered title for the research paper, using title case (capitalizing the first letter of each important word).
Include your name, instructor’s name, course name, and date in the upper left corner, double-spaced.

In-Text Citations

When quoting or paraphrasing information from sources, include an in-text citation within the text of your paper.
Use the author’s last name and the page number in parentheses at the end of the sentence, before the punctuation mark.
If the author’s name is mentioned in the sentence, only include the page number in parentheses.

Works Cited Page

List all sources cited in alphabetical order by the author’s last name.
Each entry should include the author’s name, title of the work, publication information, and medium of publication.
Use italics for book and journal titles, and quotation marks for article and chapter titles.
For online sources, include the date of access and the URL.

Here is an example of how the first page of a research paper in MLA format should look:

Headings and Subheadings

Use headings and subheadings to organize your paper and make it easier to read.
Use numerals to number your headings and subheadings (e.g. 1, 2, 3), and capitalize the first letter of each word.
The main heading should be centered and in boldface type, while subheadings should be left-aligned and in italics.
Use only one space after each period or punctuation mark.
Use quotation marks to indicate direct quotes from a source.
If the quote is more than four lines, format it as a block quote, indented one inch from the left margin and without quotation marks.
Use ellipses (…) to indicate omitted words from a quote, and brackets ([…]) to indicate added words.

Works Cited Examples

Book: Last Name, First Name. Title of Book. Publisher, Publication Year.
Journal Article: Last Name, First Name. “Title of Article.” Title of Journal, volume number, issue number, publication date, page numbers.
Website: Last Name, First Name. “Title of Webpage.” Title of Website, publication date, URL. Accessed date.

Here is an example of how a works cited entry for a book should look:

Smith, John. The Art of Writing Research Papers. Penguin, 2021.

MLA Research Paper Example

MLA Research Paper Format Example is as follows:

Your Professor’s Name

Course Name and Number

Date (in Day Month Year format)

Word Count (not including title page or Works Cited)

Title: The Impact of Video Games on Aggression Levels

Video games have become a popular form of entertainment among people of all ages. However, the impact of video games on aggression levels has been a subject of debate among scholars and researchers. While some argue that video games promote aggression and violent behavior, others argue that there is no clear link between video games and aggression levels. This research paper aims to explore the impact of video games on aggression levels among young adults.

Background:

The debate on the impact of video games on aggression levels has been ongoing for several years. According to the American Psychological Association, exposure to violent media, including video games, can increase aggression levels in children and adolescents. However, some researchers argue that there is no clear evidence to support this claim. Several studies have been conducted to examine the impact of video games on aggression levels, but the results have been mixed.

Methodology:

This research paper used a quantitative research approach to examine the impact of video games on aggression levels among young adults. A sample of 100 young adults between the ages of 18 and 25 was selected for the study. The participants were asked to complete a questionnaire that measured their aggression levels and their video game habits.

The results of the study showed that there was a significant correlation between video game habits and aggression levels among young adults. The participants who reported playing violent video games for more than 5 hours per week had higher aggression levels than those who played less than 5 hours per week. The study also found that male participants were more likely to play violent video games and had higher aggression levels than female participants.

The findings of this study support the claim that video games can increase aggression levels among young adults. However, it is important to note that the study only examined the impact of video games on aggression levels and did not take into account other factors that may contribute to aggressive behavior. It is also important to note that not all video games promote violence and aggression, and some games may have a positive impact on cognitive and social skills.

Conclusion :

In conclusion, this research paper provides evidence to support the claim that video games can increase aggression levels among young adults. However, it is important to conduct further research to examine the impact of video games on other aspects of behavior and to explore the potential benefits of video games. Parents and educators should be aware of the potential impact of video games on aggression levels and should encourage young adults to engage in a variety of activities that promote cognitive and social skills.

Works Cited:

American Psychological Association. (2017). Violent Video Games: Myths, Facts, and Unanswered Questions. Retrieved from https://www.apa.org/news/press/releases/2017/08/violent-video-games
Ferguson, C. J. (2015). Do Angry Birds make for angry children? A meta-analysis of video game influences on children’s and adolescents’ aggression, mental health, prosocial behavior, and academic performance. Perspectives on Psychological Science, 10(5), 646-666.
Gentile, D. A., Swing, E. L., Lim, C. G., & Khoo, A. (2012). Video game playing, attention problems, and impulsiveness: Evidence of bidirectional causality. Psychology of Popular Media Culture, 1(1), 62-70.
Greitemeyer, T. (2014). Effects of prosocial video games on prosocial behavior. Journal of Personality and Social Psychology, 106(4), 530-548.

Chicago/Turabian Style

Chicago/Turabian Formate is as follows:

Margins : Use 1-inch margins on all sides of the paper.
Font : Use a readable font such as Times New Roman or Arial, and use a 12-point font size.
Page numbering : Number all pages in the upper right-hand corner, beginning with the first page of text. Use Arabic numerals.
Title page: Include a title page with the title of the paper, your name, course title and number, instructor’s name, and the date. The title should be centered on the page and in title case (capitalize the first letter of each word).
Headings: Use headings to organize your paper. The first level of headings should be centered and in boldface or italics. The second level of headings should be left-aligned and in boldface or italics. Use as many levels of headings as necessary to organize your paper.
In-text citations : Use footnotes or endnotes to cite sources within the text of your paper. The first citation for each source should be a full citation, and subsequent citations can be shortened. Use superscript numbers to indicate footnotes or endnotes.
Bibliography : Include a bibliography at the end of your paper, listing all sources cited in your paper. The bibliography should be in alphabetical order by the author’s last name, and each entry should include the author’s name, title of the work, publication information, and date of publication.
Formatting of quotations: Use block quotations for quotations that are longer than four lines. Indent the entire quotation one inch from the left margin, and do not use quotation marks. Single-space the quotation, and double-space between paragraphs.
Tables and figures: Use tables and figures to present data and illustrations. Number each table and figure sequentially, and provide a brief title for each. Place tables and figures as close as possible to the text that refers to them.
Spelling and grammar : Use correct spelling and grammar throughout your paper. Proofread carefully for errors.

Chicago/Turabian Research Paper Template

Chicago/Turabian Research Paper Template is as folows:

Title of Paper

Name of Student

Professor’s Name

I. Introduction

A. Background Information

B. Research Question

C. Thesis Statement

II. Literature Review

A. Overview of Existing Literature

B. Analysis of Key Literature

C. Identification of Gaps in Literature

III. Methodology

A. Research Design

B. Data Collection

C. Data Analysis

IV. Results

A. Presentation of Findings

B. Analysis of Findings

C. Discussion of Implications

V. Conclusion

A. Summary of Findings

B. Implications for Future Research

C. Conclusion

VI. References

A. Bibliography

B. In-Text Citations

VII. Appendices (if necessary)

A. Data Tables

C. Additional Supporting Materials

Chicago/Turabian Research Paper Example

Title: The Impact of Social Media on Political Engagement

Name: John Smith

Class: POLS 101

Professor: Dr. Jane Doe

Date: April 8, 2023

I. Introduction:

Social media has become an integral part of our daily lives. People use social media platforms like Facebook, Twitter, and Instagram to connect with friends and family, share their opinions, and stay informed about current events. With the rise of social media, there has been a growing interest in understanding its impact on various aspects of society, including political engagement. In this paper, I will examine the relationship between social media use and political engagement, specifically focusing on how social media influences political participation and political attitudes.

II. Literature Review:

There is a growing body of literature on the impact of social media on political engagement. Some scholars argue that social media has a positive effect on political participation by providing new channels for political communication and mobilization (Delli Carpini & Keeter, 1996; Putnam, 2000). Others, however, suggest that social media can have a negative impact on political engagement by creating filter bubbles that reinforce existing beliefs and discourage political dialogue (Pariser, 2011; Sunstein, 2001).

III. Methodology:

To examine the relationship between social media use and political engagement, I conducted a survey of 500 college students. The survey included questions about social media use, political participation, and political attitudes. The data was analyzed using descriptive statistics and regression analysis.

Iv. Results:

The results of the survey indicate that social media use is positively associated with political participation. Specifically, respondents who reported using social media to discuss politics were more likely to have participated in a political campaign, attended a political rally, or contacted a political representative. Additionally, social media use was found to be associated with more positive attitudes towards political engagement, such as increased trust in government and belief in the effectiveness of political action.

V. Conclusion:

The findings of this study suggest that social media has a positive impact on political engagement, by providing new opportunities for political communication and mobilization. However, there is also a need for caution, as social media can also create filter bubbles that reinforce existing beliefs and discourage political dialogue. Future research should continue to explore the complex relationship between social media and political engagement, and develop strategies to harness the potential benefits of social media while mitigating its potential negative effects.

Vii. References:

Delli Carpini, M. X., & Keeter, S. (1996). What Americans know about politics and why it matters. Yale University Press.
Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin.
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.
Sunstein, C. R. (2001). Republic.com. Princeton University Press.

IEEE (Institute of Electrical and Electronics Engineers) Format

IEEE (Institute of Electrical and Electronics Engineers) Research Paper Format is as follows:

Title : A concise and informative title that accurately reflects the content of the paper.
Abstract : A brief summary of the paper, typically no more than 250 words, that includes the purpose of the study, the methods used, the key findings, and the main conclusions.
Introduction : An overview of the background, context, and motivation for the research, including a clear statement of the problem being addressed and the objectives of the study.
Literature review: A critical analysis of the relevant research and scholarship on the topic, including a discussion of any gaps or limitations in the existing literature.
Methodology : A detailed description of the methods used to collect and analyze data, including any experiments or simulations, data collection instruments or procedures, and statistical analyses.
Results : A clear and concise presentation of the findings, including any relevant tables, graphs, or figures.
Discussion : A detailed interpretation of the results, including a comparison of the findings with previous research, a discussion of the implications of the results, and any recommendations for future research.
Conclusion : A summary of the key findings and main conclusions of the study.
References : A list of all sources cited in the paper, formatted according to IEEE guidelines.

In addition to these elements, an IEEE research paper should also follow certain formatting guidelines, including using 12-point font, double-spaced text, and numbered headings and subheadings. Additionally, any tables, figures, or equations should be clearly labeled and referenced in the text.

AMA (American Medical Association) Style

AMA (American Medical Association) Style Research Paper Format:

Title Page: This page includes the title of the paper, the author’s name, institutional affiliation, and any acknowledgments or disclaimers.
Abstract: The abstract is a brief summary of the paper that outlines the purpose, methods, results, and conclusions of the study. It is typically limited to 250 words or less.
Introduction: The introduction provides a background of the research problem, defines the research question, and outlines the objectives and hypotheses of the study.
Methods: The methods section describes the research design, participants, procedures, and instruments used to collect and analyze data.
Results: The results section presents the findings of the study in a clear and concise manner, using graphs, tables, and charts where appropriate.
Discussion: The discussion section interprets the results, explains their significance, and relates them to previous research in the field.
Conclusion: The conclusion summarizes the main points of the paper, discusses the implications of the findings, and suggests future research directions.
References: The reference list includes all sources cited in the paper, listed in alphabetical order by author’s last name.

In addition to these sections, the AMA format requires that authors follow specific guidelines for citing sources in the text and formatting their references. The AMA style uses a superscript number system for in-text citations and provides specific formats for different types of sources, such as books, journal articles, and websites.

Harvard Style

Harvard Style Research Paper format is as follows:

Title page: This should include the title of your paper, your name, the name of your institution, and the date of submission.
Abstract : This is a brief summary of your paper, usually no more than 250 words. It should outline the main points of your research and highlight your findings.
Introduction : This section should introduce your research topic, provide background information, and outline your research question or thesis statement.
Literature review: This section should review the relevant literature on your topic, including previous research studies, academic articles, and other sources.
Methodology : This section should describe the methods you used to conduct your research, including any data collection methods, research instruments, and sampling techniques.
Results : This section should present your findings in a clear and concise manner, using tables, graphs, and other visual aids if necessary.
Discussion : This section should interpret your findings and relate them to the broader research question or thesis statement. You should also discuss the implications of your research and suggest areas for future study.
Conclusion : This section should summarize your main findings and provide a final statement on the significance of your research.
References : This is a list of all the sources you cited in your paper, presented in alphabetical order by author name. Each citation should include the author’s name, the title of the source, the publication date, and other relevant information.

In addition to these sections, a Harvard Style research paper may also include a table of contents, appendices, and other supplementary materials as needed. It is important to follow the specific formatting guidelines provided by your instructor or academic institution when preparing your research paper in Harvard Style.

Vancouver Style

Vancouver Style Research Paper format is as follows:

The Vancouver citation style is commonly used in the biomedical sciences and is known for its use of numbered references. Here is a basic format for a research paper using the Vancouver citation style:

Title page: Include the title of your paper, your name, the name of your institution, and the date.
Abstract : This is a brief summary of your research paper, usually no more than 250 words.
Introduction : Provide some background information on your topic and state the purpose of your research.
Methods : Describe the methods you used to conduct your research, including the study design, data collection, and statistical analysis.
Results : Present your findings in a clear and concise manner, using tables and figures as needed.
Discussion : Interpret your results and explain their significance. Also, discuss any limitations of your study and suggest directions for future research.
References : List all of the sources you cited in your paper in numerical order. Each reference should include the author’s name, the title of the article or book, the name of the journal or publisher, the year of publication, and the page numbers.

ACS (American Chemical Society) Style

ACS (American Chemical Society) Style Research Paper format is as follows:

The American Chemical Society (ACS) Style is a citation style commonly used in chemistry and related fields. When formatting a research paper in ACS Style, here are some guidelines to follow:

Paper Size and Margins : Use standard 8.5″ x 11″ paper with 1-inch margins on all sides.
Font: Use a 12-point serif font (such as Times New Roman) for the main text. The title should be in bold and a larger font size.
Title Page : The title page should include the title of the paper, the authors’ names and affiliations, and the date of submission. The title should be centered on the page and written in bold font. The authors’ names should be centered below the title, followed by their affiliations and the date.
Abstract : The abstract should be a brief summary of the paper, no more than 250 words. It should be on a separate page and include the title of the paper, the authors’ names and affiliations, and the text of the abstract.
Main Text : The main text should be organized into sections with headings that clearly indicate the content of each section. The introduction should provide background information and state the research question or hypothesis. The methods section should describe the procedures used in the study. The results section should present the findings of the study, and the discussion section should interpret the results and provide conclusions.
References: Use the ACS Style guide to format the references cited in the paper. In-text citations should be numbered sequentially throughout the text and listed in numerical order at the end of the paper.
Figures and Tables: Figures and tables should be numbered sequentially and referenced in the text. Each should have a descriptive caption that explains its content. Figures should be submitted in a high-quality electronic format.
Supporting Information: Additional information such as data, graphs, and videos may be included as supporting information. This should be included in a separate file and referenced in the main text.
Acknowledgments : Acknowledge any funding sources or individuals who contributed to the research.

ASA (American Sociological Association) Style

ASA (American Sociological Association) Style Research Paper format is as follows:

Title Page: The title page of an ASA style research paper should include the title of the paper, the author’s name, and the institutional affiliation. The title should be centered and should be in title case (the first letter of each major word should be capitalized).
Abstract: An abstract is a brief summary of the paper that should appear on a separate page immediately following the title page. The abstract should be no more than 200 words in length and should summarize the main points of the paper.
Main Body: The main body of the paper should begin on a new page following the abstract page. The paper should be double-spaced, with 1-inch margins on all sides, and should be written in 12-point Times New Roman font. The main body of the paper should include an introduction, a literature review, a methodology section, results, and a discussion.
References : The reference section should appear on a separate page at the end of the paper. All sources cited in the paper should be listed in alphabetical order by the author’s last name. Each reference should include the author’s name, the title of the work, the publication information, and the date of publication.
Appendices : Appendices are optional and should only be included if they contain information that is relevant to the study but too lengthy to be included in the main body of the paper. If you include appendices, each one should be labeled with a letter (e.g., Appendix A, Appendix B, etc.) and should be referenced in the main body of the paper.

APSA (American Political Science Association) Style

APSA (American Political Science Association) Style Research Paper format is as follows:

Title Page: The title page should include the title of the paper, the author’s name, the name of the course or instructor, and the date.
Abstract : An abstract is typically not required in APSA style papers, but if one is included, it should be brief and summarize the main points of the paper.
Introduction : The introduction should provide an overview of the research topic, the research question, and the main argument or thesis of the paper.
Literature Review : The literature review should summarize the existing research on the topic and provide a context for the research question.
Methods : The methods section should describe the research methods used in the paper, including data collection and analysis.
Results : The results section should present the findings of the research.
Discussion : The discussion section should interpret the results and connect them back to the research question and argument.
Conclusion : The conclusion should summarize the main findings and implications of the research.
References : The reference list should include all sources cited in the paper, formatted according to APSA style guidelines.

In-text citations in APSA style use parenthetical citation, which includes the author’s last name, publication year, and page number(s) if applicable. For example, (Smith 2010, 25).

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Exploring the Relationship Between Early Life Exposures and the Comorbidity of Obesity and Hypertension: Findings from the 1970 The British Cohort Study (BCS70)

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ORCID record for S Stannard
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Background Epidemiological research commonly investigates single exposure-outcome relationships, while children’s experiences across a variety of early lifecourse domains are intersecting. To design realistic interventions, epidemiological research should incorporate information from multiple risk exposure domains to assess effect on health outcomes. In this paper we identify exposures across five pre-hypothesised childhood domains and explored their association to the odds of combined obesity and hypertension in adulthood.

Methods We used data from 17,196 participants in the 1970 British Cohort Study. The outcome was obesity (BMI of ≥30) and hypertension (blood pressure>140/90mm Hg or self-reported doctor’s diagnosis) comorbidity at age 46. Early life domains included: ‘prenatal, antenatal, neonatal and birth’, ‘developmental attributes and behaviour’, ‘child education and academic ability’, ‘socioeconomic factors’ and ‘parental and family environment’. Stepwise backward elimination selected variables for inclusion for each domain. Predicted risk scores of combined obesity and hypertension for each cohort member within each domain were calculated. Logistic regression investigated the association between domain-specific risk scores and odds of obesity-hypertension, controlling for demographic factors and other domains.

Results Adjusting for demographic confounders, all domains were associated with odds of obesity-hypertension. Including all domains in the same model, higher predicted risk values across the five domains remained associated with increased odds of obesity-hypertension comorbidity, with the strongest associations to the parental and family environment domain (OR1.11 95%CI 1.05-1.18) and the socioeconomic factors domain (OR1.11 95%CI 1.05-1.17).

Conclusions Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on obesity-hypertension prevalence which are known risk factors for further morbidity including cardiovascular disease.

Competing Interest Statement

R.O. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, member of the NICE Decision Support Unit (DSU), and associate member of the NICE Technical Support Unit (TSU). She has served as a paid consultant to the pharmaceutical industry and international reimbursement agencies, providing unrelated methodological advice. She reports teaching fees from the Association of British Pharmaceutical Industry (ABPI). R.H. is a member of the Scientific Board of the Smith Institute for Industrial Mathematics and System Engineering.

Funding Statement

This work is part of the multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) project which is supported by the National Institute for Health Research (NIHR203988). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics approval for this work has been obtained from the University of Southampton Faculty of Medicine Ethics committee (ERGO II Reference 66810).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability Statement

The BCS70 datasets generated and analysed in the current study are available from the UK Data Archive repository (available here: http://www.cls.ioe.ac.uk/page.aspx?&sitesectionid=795 ).

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Original research article, microwave biosensor for the detection of growth inhibition of human liver cancer cells at different concentrations of chemotherapeutic drug.

1 School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi, China
2 State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
3 Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing, China
4 School of Biotechnology, the Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Cytotoxicity assays are crucial for assessing the efficacy of drugs in killing cancer cells and determining their potential therapeutic value. Measurement of the effect of drug concentration, which is an influence factor on cytotoxicity, is of great importance. This paper proposes a cytotoxicity assay using microwave sensors in an end-point approach based on the detection of the number of live cells for the first time. In contrast to optical methods like fluorescent labeling, this research uses a resonator-type microwave biosensor to evaluate the effects of drug concentrations on cytotoxicity by monitoring electrical parameter changes due to varying cell densities. Initially, the feasibility of treating cells with ultrapure water for cell counting by a microwave biosensor is confirmed. Subsequently, inhibition curves generated by both the CCK-8 method and the new microwave biosensor for various drug concentrations were compared and found to be congruent. This agreement supports the potential of microwave-based methods to quantify cell growth inhibition by drug concentrations.

1 Introduction

Cytotoxicity assays are pivotal in evaluating cellular damage induced by drugs, playing a critical role in the drug development process and safety evaluation ( Parboosing et al., 2017 ; Zhang and Wan, 2022 ). These assays facilitate the determination of a drug’s safety profile, therapeutic window, and potential side effects, thus informing drug design, judicious usage, and toxicity risk assessment. They are instrumental in detecting adverse effects and providing essential data for the secure administration of drugs ( Niles et al., 2008 ; Vaucher et al., 2010 ).

The relationship between drug concentration, an independent factor influencing cytotoxicity ( Chan et al., 2002 ; Radko et al., 2013 ), and cytotoxicity is essential to optimize therapeutic efficacy and minimize adverse effects. Understanding the dose-response relationship is pivotal for researchers to strike a delicate balance between drug efficacy and safety, thereby ensuring judicious drug utilization that curtails potential risks. Cancer cells differ from normal cells in many ways, one of which is that they grow and divide very rapidly. In response to this characteristic, a number of cytotoxic drugs have been developed that target rapidly proliferating cancer cells and inhibit their growth and proliferation by interfering with their DNA synthesis or cell division processes ( McQuade et al., 2017 ; Dongsar et al., 2023 ). However, cytotoxic drugs do not completely discriminate between cancer cells and normal cells ( Tofzikovskaya et al., 2015 ). Mitomycin-C is an example of a cell cycle-specific chemotherapeutic agent widely used in oncology and cytotoxicity research ( Tomasz, 1995 ; Zhang et al., 2019 ; Park et al., 2021 ), predominantly acting on the G2 and M phases to impede DNA synthesis and cell division, thereby arresting cancer cell proliferation. Despite its efficacy against various cancer cell types, mitomycin-c’s potential toxicity to normal cells necessitates rigorous dose regulation and vigilant monitoring for adverse effects. Consequently, assessing the drug concentration-inhibition relationship is an indispensable component of cytotoxicity studies.

To accurately evaluate drug impacts on cell viability, two common techniques are employed: real-time cellular analysis (RTCA) and CCK-8 assays ( Cai et al., 2019 ). RTCA offers real-time, non-invasive monitoring of cellular dynamics, but with limited application scope and higher equipment costs ( Yan et al., 2018 ). Conversely, the CCK-8 assay, a standard in cytotoxicity tests, facilitates straightforward colorimetric measurements and is versatile across various cell lines ( Wang et al., 2015 ; Liu et al., 2018 ; Wang et al., 2018 ). However, to ensure a sufficient reaction, the CCK-8 method requires a certain incubation time for the reaction, typically 1∼4 h.

While established cytotoxicity assays like CCK-8 and real-time cellular assays are well-developed, ongoing research is delving into novel assays tailored for diverse cellular contexts and specific experimental requirements. The investigation of cytotoxic mechanisms warrants detailed analysis in certain studies, whereas others prioritize the rapidity and precision of the assay’s readouts. Moreover, optical and electrical measurements can often complement each other’s results in the field of biosensing ( He et al., 2023 ). Microwave biosensors, as a new type of biosensor, are highly sensitive and correspondingly fast (their response time is usually only a few seconds to a few mins), allowing real-time results to be obtained in a short period of time ( Narang et al., 2018 ; Gao et al., 2021 ). Microwave biosensors’ compactness and lightweight design indeed make them ideal for portable device production. Their seamless integration with electronic circuits, coupled with appropriate algorithms, can lead to intelligent data processing products. To date, no attempt has been made to detect drug cytotoxicity using microwave resonance sensors. If the microwave biosensor can be used for cytotoxicity detection, it can complement the original method in terms of advantages and disadvantages. This wound also further broaden the application areas of microwave detection. Microwave biosensors for measuring cytotoxicity have the advantages of eliminating the need for cell staining, rapid detection, low cost, easy integration with matching circuits, and small sample size. Microwave sensors based on resonant elements are very sensitive to the dielectric constant and loss angle tangent of the surrounding medium ( Muñoz-Enano et al., 2020 ), and have been widely used in the fields of biosensing. Researchers have demonstrated that it has promising applications in bacteria detection ( Narang et al., 2018 ; Jain et al., 2020 ; Jain et al., 2021 ), blood glucose detection ( Yilmaz et al., 2019 ; Kandwal et al., 2020 ; Nazli Kazemi anLight, 2023 ), and many other areas. Since the key to the endpoint method of evaluating drug cytotoxicity is to determine the number of surviving cells at the end of the experiment ( Adan et al., 2016 ), and there have been studies on the differences in dielectric properties of cell solutions at different concentrations ( Chen et al., 2014 ), it has a certain degree of feasibility to do cytotoxicity testing with microwave sensors.

In this paper, we have designed and fabricated a microwave biosensor based on the integrated passive device (IPD) fabrication technology. IPD integrates different passive components (inductors, capacitors, resistors) in a single subcomponent, which is characterized by a small linewidth, precise substrate control, a high degree of integration and fewer parasitic effects ( Yu et al., 2019 ; Chu et al., 2020 ). Moreover, IPDs demonstrate enhanced stability compared to capacitive or resistive sensors ( Yu et al., 2021 ). The consolidation of multiple passive components onto a single chip allows IPDs to conserve space, diminish energy consumption, bolster system reliability and accuracy of measurements, and ease the transition to productization. Employing this biosensor, we assessed the impact of concentration on cytotoxicity using HepG2 cells as the model and Mitomycin-c as the chemotherapeutic agent. We determined OD450 values via the CCK-8 assay, which is the biological gold standard ( Zhou et al., 2018 ), as a control group for parallel experiments and verified the feasibility of cytotoxicity experiments using microwave sensors by mapping and comparing the curves of the two groups. In addition, we treated the cells with ultrapure water instead of phosphate buffered saline (PBS) in this experiment to verify the feasibility of this treatment in the microwave biosensor cell number measurement experiments.

2 Materials and methods

2.1 sensor design and analysis.

The proposed biosensor is a microwave IPD resonator, consisting of a spiral inductor and an interdigital capacitor, where changes in the electrical parameters of the surrounding medium, mainly the dielectric constant and the loss angle tangent can cause changes in the resonant frequency or the amplitude of the resonance peak. When designing microwave resonators, the relevant parameters and performance are usually adjusted by the capacitance section ( Zhu and Abbosh, 2016 ; Xu and Zhu, 2017 ). The spiral inductor of the proposed microwave sensor is pre-designed by our group ( Wang et al., 2023 ) and this work focuses on the design, optimization and simulation of the interdigital capacitor. By adjusting the corresponding capacitance structure, we can adjust the frequency sensitivity and amplitude sensitivity of the resonator. In the design of the interdigital capacitive structure, three schemes are considered, respectively, in a cross-shaped central periphery equally spaced increase of 1-turn, 2-turn and 3-turn copper strip lines as shown in Figures 1A–I, B-I, and C-I . The reflection coefficient (S 11 ) of the three resonators and the variation of the resonance peak amplitude in different loss angle tangent environments are simulated in the Advanced Design System 2020 (ADS). The Eq. (1) shows that the permittivity of a sample can be obtained by adding the real and imaginary permittivity:

Figure 1 . Simulation results of the sensor. (A) Interdigital capacitor with 1-turn, (A–I) structure, (A-II) S 11 , (A-III) resonance peak amplitude in different loss angle tangent. (B) Interdigital capacitor with 2-turns, (B–I) structure, (B-II) S 11 , (B-III) resonance peak amplitude in different loss angle tangent. (C) Interdigital capacitor with 3-turns, (C–I) structure, (C-II) S 11 , (C-III) resonance peak amplitude in different loss angle tangent.

The loss angle tangent is calculated from Eq. 2 :

Samples with varying cell concentrations can be characterized by using different values of the loss angle tangent. A change in the loss angle tangent indicates a change in the complex dielectric constant, which in turn affects the S 11 of the microwave resonator. It can be seen from Figures 1A–II , B-II, and C-II that as the number of turns increases, the resonant frequency decreases, the bandwidth decreases and the Q value decreases. High Q represents high energy storage capacity and frequency selectivity. In terms of sensitivity to the loss angle tangent, the structure of 2-turn shows the best performance as illustrated in Figures 1A–III , B-III, and C-III. Since the resonance amplitude is usually the preferred metric for this type of detection relative to the resonance frequency ( Jain et al., 2021 ), and combined with factors such as the size of the detection area, interdigital capacitor with 2-turns was finally selected as the biosensor.

Figure 2A delineates the capacitive section’s architecture and precise dimensions. Encircling the device, a spiral inductor integrated with air-bridge structures is observed, while at its nucleus lies an interdigital capacitor, composed of strip wires coiled around a cruciform framework. The strip lines boast a uniform size and interspace of 20 μm. Figure 2B shows the longitudinal layer structure of the sensor, from top to bottom, with a 4.5/0.5 µm Cu/Au top layer, a 1.8 μm copper interconnect layer containing air bridge structure which were introduced in the spiral inductor to increase the mutual inductance and decrease the signal transmission loss in the inductor, a 4.5/0.5 µm Cu/Au bottom layer, a 0.2 μm thick nitride dielectric layer with relative dielectric constant of 7.5 and a loss angle tangent of 0.0036, a 200 μm thick GaAs substrate layer with relative dielectric constant of 12.85 and a loss angle tangent of 0.0028. For the fabrication of our proposed biosensor, seed metal (Ti/Au) is sputtered with the thicknesses of 20/80 nm as for strengthened metallic adhesion. In the electroplating process, gold and copper are tightly bonded through the plating process and have excellent corrosion resistance and do not easily diffuse into the solution, thus they do not interfere with the cytotoxicity analysis of cells. The electric field condition of this resonator is simulated in High Frequency Structure Simulator 19.1(HFSS), and its horizontal E-field strength is shown in Figure 2C , where the E-field strength reaches 10 6 V/m in its core sensitive region. The highest electric field strengths reported so far in the paper are around 10 5 V/m ( Zarifi et al., 2017 ; Kumar et al., 2020 ). The device’s notably higher electric field strengths suggest enhanced penetration and sensitivity. Considering the actual measured solution droplet size, the longitudinal field strength distribution is also simulated, and the results are shown in Figure 2D , illustrating that the sensitive region can still achieve an electric field strength of 10 5 V/m at a height of 50 μm. High electric field strength in the horizontal and vertical directions reveals the good penetration capability of the device. Consequently, this allows for the use of larger droplet volumes when applying sample droplets, effectively minimizing random sampling errors. Figure 2E shows the equivalent circuit diagram of the device. The capacitance of the oxide layer between the base and the metal can be denoted as C ox , the resistance between the substrate and the ground can be denoted as R sub , the capacitance can be denoted as C sub , the parasitic resistance of the inductor can be denoted as R L , the parasitic conductance of the capacitance can be denoted as G . Through the equivalent circuit transformation, the whole device can be regarded as an LC resonator. The complex dielectric constant properties of the cell solution can be modeled using the Debye equation. The relationship between the measured microwave parameters of the cell solution and the complex dielectric constant can be expressed by Eq. (3) as ( Withayachumnankul et al., 2013 )

where △ ε ′ = ε s ′ − ε r ′ , △ ε ″ = ε s ″ − ε r ″ , △ f 0 = f s − f r and △ S 11 = S 11 s − S 11 r are the differences between the sample (with subscript s ) and the reference (with subscript r ) values, m 11 , m 12 , m 21 , m 22 is the parameters to be determined. In this experiment, a change in cell concentration would cause a change in the loss angle tangent, thus causing a change in △ S 11 .

Figure 2 . Device structure analysis and electric field simulation. (A) Overall device structure and dimensions of interdigital capacitance. (B) The hierarchical structure of the device. (C) Surface electric field distribution of devices. (D) Vertical electric field distribution. (E) Equivalent circuit diagram.

2.2 Preparation of biological sample

HepG2 cell line is used as the experimental cells which were purchased from the cell bank of the Chinese Academy of Science (Shanghai, China). It is a human hepatocellular carcinoma cell line commonly used in the study of molecular mechanisms, drug screening and treatment of liver cancer ( Elkady et al., 2022 ). The entire experimental procedure is illustrated in Figure 3 . After completing the cell resuscitation, we first performed a pre-experiment using the fabricated microwave biosensor for cell number measurement. We inoculated cells into rows A, B, D, and E of a 96-well plate with a concentration gradient of 100 cells per well to 200,000 cells per well in two-fold increments, and added.

Figure 3 . Cell culture and handling, addition of drugs and pre-preparation for measurements.

Dulbecco’s modified eagle medium (DMEM) to make them adherent to the bottom by incubating them for 24 h in a CO 2 incubator at 37 °C. After removing them from the incubator, we pipetted the DMEM from the A and B rows and washed them with PBS. After that, in row A, trypsin treatment was used to dissociate the cells from the bottom, and then 100 μL of PBS was injected into each well; in row B, the same trypsin treatment was used, and then 100 μL of ultrapure water was injected into each well. Rows D and E are used as backup groups. The PBS, trypsin solution and DMEM used in the experiments were purchased from Sangon Biotech (Shanghai, China). The cell culture incubator was purchased from Thermofishe (United States). Normally, in cell number experiments, cells are treated in PBS.In this study, ultrapure water was utilized to treat Group B based on several key considerations. In order to maintain an isotonic state with the cytosol, the ionic concentration of PBS buffer and cytosol is similar. This would result in the cells and the PBS potentially exhibiting similar electrical parameter characteristics, which will lead to a narrowing of the differences in electrical parameters caused by the concentration of the cells. Conversely, the contrast in ionic concentration between ultrapure water and the cell solution is likely to amplify the solution’s electrical parameter changes due to cellular quantity. On the other hand, cell water uptake and cell fluid exudation can result in a more uniform ionic distribution of the solution, thus mitigating random errors linked to small sample sizes. After completing the pre-experimental validation, we seeded 50,000 cells per well on a new 96-well plate, inoculated on row C, D and E as three parallel groups, and after 24 h of CO 2 thermostatic incubation for cell adhesion, drug administration commenced. In this experiment, we used mitomycin-c as an inhibitor of cell growth. Mitomycin-c was selected as the cell growth inhibitor for this experiment. It was initially dissolved in dimethyl sulfoxide before being prepared into a stock solution at various concentrations. This stock solution was then serially diluted with DMEM to create a two-fold concentration gradient ranging from 1.7 μmol/L to 40 μmol/L. Subsequently, 200 μL of DMEM containing varying concentrations of mitomycin-c was added to each well of the 96-well plate and incubated at 37°C in a CO 2 incubator for 48 h. Repeat the above steps and prepare the same 3 rows of cells on a new 96-well plate, with one set for OD450 optical measurements and the other set for microwave measurements. For microwave measurement groups, remove the DMEM with a pipette, wash it with PBS, inject 100 μL of ultrapure water into each well. After a period of resting, pipette 1.5 μL of solution and drop it on the sensor for detection. Since the proposed microwave biosensor performs cytotoxicity detection mainly by detecting the concentration of ions contained in the cells, it is unable to distinguish between live and dead cells. Therefore, it is important to ensure that dead cells are cleaned as completely as possible before measurement. Additionally, the ions in the drug can also affect the measurements, so it is important to ensure that the drug is completely purified. The mitomycin-c and dimethyl sulfoxide used were purchased from MedChemExpress (Shanghai, China).

2.3 Experimental environment

The experimental apparatus was positioned on an anti-static mat and comprised a Vector Network Analyzer (VNA, Ceyear, 3656B), the IPD device, coaxial cables, samples, and a pipette, as depicted in Figure 4A . At the heart of the IPD device lies the microwave resonator, detailed microstructurally in Figure 4C . The resonator’s two ports are connected by bonding wire to the corresponding input and output matching wires on the printed circuit board. Figure 4B schematically illustrates the assembled sensor. Its bottom is an aluminum block with screw holes for fixing holes, and the chip is first fixed on top of the aluminum block by screws, and then connected to the coaxial cable of the VNA through the Small A Type connector fixed on both sides. This meticulous assembly ensures the chip remains horizontally stable, mitigating positional errors. The coaxial cable itself is taped to the table to reduce measurement disruptions from any movement. During the measurement, 1.5 μL of solution was added to the middle sensing area with a pipette. To ensure uniform distribution and mitigate the risk of sample settling, each sample drawn from a 96-well plate via pipette is agitated by employing a larger pipette tip. In subsequent sample drops, it was found that when the droplet volume was equal to 2 μL or larger, the droplets were easy to be dispersed irregularly on the surface of the device leading to measurement failure due to destruction of the surface tension of the droplets. After each measurement, the liquid was sucked up with absorbent paper and was cleaned several times with ultrapure water to return the S 11 to the initial values to ensure that the next experiment was not affected. Since temperature and humidity affect the performance of semiconductor devices, we control and measure the temperature and humidity values, the measurements were carried out at an ambient temperature of 20°C∼21 °C and a humidity of 47 %RH∼48 %RH.

Figure 4 . Measuring platforms and fabricated sensor. (A) The measurement environment. (B) Device structure and assembly schematic. (C) Microscope image of the proposed sensor.

3 Results and discussion

3.1 pre-experimental results of cell number measurements.

Figure 5 shows the overall results of the cell number measurement experiment. In the cell number measurement pre-experiment, pictures of cells with concentration gradients from 6.4×10 4 /mL to 2×10 6 /mL were taken under the microscope as shown in Figure 5A∼F , which showed healthy growth and a clear concentration gradient. The cells with a concentration gradient from 1×10 4 /mL to 3.2×10 4 /mL did not show a marked difference due to the limited cell numbers, similar to 6.4×10 4 /mL. Figure 5G illustrates the cellular morphology in PBS, where a transition from wall-adherent irregular shapes to more defined round or ovoid forms is observed, predominantly existing as either single entities or aggregated clusters. Under this circumstance, a dynamic equilibrium of ion and water molecule exchange is established between the intracellular and extracellular environments, resulting in comparable ion concentrations. Figure 5H shows the status of the cells after 5 mins of exposure to ultrapure water. Due to the lower osmotic pressure of pure water compared to the cells, water enters the cells, causing them to swell or even dissolve. Cells lose their original morphological characteristics in pure water and become flattened, deformed or ruptured. This can lead to spillage of cell contents dispersed in ultrapure water. The measurements of the S 11 near the resonance peak of ten quantities of cells after treatment with ultrapure water are shown in Figure 5I . The peak value of the S 11 decreases with the increase of cell concentration. These measurements were plotted as points in Origin. It can be found that when the number of cells is too low (lower than 6.4 × 10 4 /mL), the measurements of microwave amplitude are similar, showing a deviation from the other groups and are similar to the measurements of ultrapure water. This may be due to inadequate cytosol exchange with external components when cell numbers are low, and the aspirated 1.5 μL solution may not contain cell membrane components. After selecting the mean of multiple measurements, we performed a linear fit to the mean data on the last six data as shown in Figure 5J . The error bars are based on the mean value, and the relationship between the amplitude of the resonance peak and the concentration of the cells can be characterized by y = 2.58549 × 10 −7 x-25.70623. R 2 is 0.99874, showing a good linear relationship. The corresponding detection and quantification limits (LOD&LOQ) of the proposed devices was calculated on the basis of following Eqs ( 4 , 5 ) ( Qiang et al., 2017 ) as 1.41×10 5 /mL and 4.23×10 5 /mL, respectively.

where SD is the standard deviation of the frequency response and m is the slope of the regression line. That means, the lowest amount of analyte in a sample which can be detected but not necessarily quantitated as an exact value is 1.41×10 5 /mL, the lowest amount of analyte in a sample which can be quantitatively determined is 4.23×10 5 /mL. This experiment demonstrated that there is a linear relationship between the magnitude of the amplitude under the microwave resonator and the number of cells. Specifically, when the number of cells exceeds a certain threshold (6.4×10 4 /mL), the solution of adherent cells treated with ultrapure water shows this relationship. This experiment confirms that using this microwave sensor to measure cytotoxicity as an endpoint is feasible.

Figure 5 . Results of the pre-experiment on cell number measurement. Cells cultured in DMEM at a concentration of (A) 6.4×10 4 /mL, (B) 1.25×10 4 /mL, (C) 2.5×10 5 /mL, (D) 5×10 5 /mL, (E) 1×10 6 /mL, (F) 2×10 6 /mL. (G) Cells in PBS, and (H) cells in ultrapure water. (I) S 11 of different concentrations in ultrapure water. (J) Linear fitted results of cell concentration and magnitude in resonant frequency.

3.2 Measurements from drug inhibition experiments

The results of the drug concentration cytotoxicity assay measurements are presented in Figure 6 . Figure 6A demonstrates the cell growth after 48 h of culture with drug concentration ranging from 1.7 μM to 12.65 μM. It can be clearly seen that the number of live cells gradually decreased as the drug concentration increased, and the inhibition of cell growth by the drug can be assessed from the number of surviving live cells. The inhibitory capacity of mitomycin reaches its maximum at drug concentrations of approximately 9.5 μM. Higher concentrations have similar inhibitory effects to 9.5 μM. The cells were subjected to OD450 measurements, depicting the curves in Figure 6B . In addition, the curves of cell concentration and OD450 values were measured for HepG2, and the results are shown in Figure 6C which is similar to the results of the OD450 measurement of cell number in Figure 5J . Since OD450 values have a good linear relationship with cell concentration, OD450 measurements can be equated to cell concentration. Microwave resonance peak amplitude measurements were performed after ultrapure water treatment. A set of near-mean measurements was selected and their S 11 are plotted in Figure 6D . It can be observed that the amplitude of the resonance peak decreases by approximately 0.45 dB as the drug concentration increases from 1.70 μM to 12.65 μM. The relationship between amplitude and drug concentration was plotted in Figure 6E after an equal number of measurements were taken in three parallel groups and the mean value was selected. It can be seen that the microwave resonance amplitude measurements have similar results to the OD450 measurements. At higher drug concentrations, the resonance amplitude tends to a stable value. Therefore, it is feasible to use the resonance amplitude curve as an assessment index of drug toxicity. Various in vitro cytotoxicity assays are currently available including chromium release, bioluminescence, impedance, and flow cytometry ( Kiesgen et al., 2021 ), most of which are based on chemical methods such as fluorescent labelling, optical densitometry and radioactivity determination. These methods have their characteristics and scope of application as well as limitations, microwave sensor methods introduce a new possibility for cytotoxicity determination, and their comparison is presented in Table 1 . These methods can be divided into two main categories, optical and electrical, covering a wide range of cellular measurements. In terms of device size, microwave biosensors have the advantage of being small. Microwave methods are on a similar scale to flow cytometry in terms of the concentration of cells that can be processed. Microwave methods are characterized by a tiny sample capacity (0.8 μL∼ 2 μL) in addition to inheriting the advantages of electrical methods that do not require staining of cells.

Figure 6 . Results of cytotoxicity assay for different concentrations of drugs. (A) Microscopic images of cells cultured at different drug concentrations for 48 h and washed with PBS buffer to remove dead cells. (B) OD450 value detection of live cells after 48 h of action with different concentrations of Mitomycin-c. (C) Measurement results of linear relationship between HepG2 concentration and OD450 value. (D) Measurement results of S 11 after drug concentration 1.7 μM∼40.0 μM action. (E) Microwave amplitude detection of living cells in aqueous solutions of Mitomycin-c with different concentrations after 48 h of action.

Table 1 . Summaries of existing cytotoxicity assays.

3.3 Experimental principles

The measurement mechanism of this experiment is divided into two main parts. The first part is cellular water uptake and subsequent rupture as shown in Figure 7A . When a cell is placed in ultrapure water, the concentration of the solution inside the cell is relatively high, while the concentration of the solution in ultrapure water is extremely low. Osmotic forces drive water molecules from the exterior into the cell, causing a volumetric expansion of the cell, a phenomenon termed cellular water absorption. However, if the cell absorbs more water molecules than it can hold, the increased internal pressure may cause the cell membrane to rupture. This typically happens when the cell membrane’s elastic limit is surpassed. After mechanical shaking, the broken cell membrane and various ions within the cell are dispersed relatively uniformly in solution. Differences in the number of cells can lead to differences in the final total ion concentration of the solution, as the cells are treated with equal amounts of ultrapure water. It should be noted that the number of cells should not be too high, otherwise they may not all rupture completely after absorbing water.

Figure 7 . Experimental principles. (A) Cell rupture in ultrapure water. (B) Measurement of sample in biosensor.

The second part is the principle of sample detection by the microwave biosensor as shown in Figure 7B . The cytosol contains a variety of ions, with sodium, potassium and chloride ions making up a large proportion. The effect of ion concentration on dielectric properties has been studied extensively, e.g., an increase in the concentration of sodium chloride leads to a decrease in the loss angle tangent ( Wang et al., 2013 ; Dandan et al., 2015 ). When the concentrations of sodium chloride and potassium chloride solutions are below a certain value, the dielectric properties of the solutions are similar to those of pure water, and only when they are above a certain value do the dielectric properties show a clear trend ( Eldamak et al., 2020 ). The loss angle tangent describes the nature of the ability of a material to absorb electromagnetic waves and is related to the energy loss in the material. As the concentration of a solution increases, so does the number of solute molecules or ions. At lower concentrations, the ions in the solution have a weaker ability to absorb electromagnetic waves, resulting in a larger loss angle tangent. However, as the concentration increases, the polarization effect of the ions in the solution increases, making the solution less able to absorb electromagnetic waves, resulting in a decrease in the loss angle tangent. Changes in the loss angle tangent affect the degree of microwave attenuation in the solution and the resonance peak of the resonator. When the solution is dropped onto the capacitive area of the microwave resonator, the medium surrounding the capacitive area changes, and the microwave biosensor detects this change sensitively and rapidly. The VNA sends a microwave signal over a set frequency range and measures the amplitude and phase of the reflected and transmitted signals. By varying the frequency and recording the corresponding signal response, data on the S 11 can be obtained. Further, the VNA can be connected to a computer to efficiently detect changes in the analyzed parameters using the corresponding software.

4 Conclusion

In this work, a microwave resonant sensor based on an integrated passive device is presented. The device can be used for cell number detection and further, for the assessment of the degree of cell growth inhibition by drug concentration. The sensor’s capability to detect cytotoxicity was validated against the biological gold standard, the CCK-8 assay. Unlike the usual PBS treatment of cells, ultrapure water was used to treat the cells in this experiment, offering an innovative approach for cell sensing via microwave technology. This novel method provides rapid, precise, and miniaturized cytotoxicity assessments, suitable for various applications. Future enhancements should concentrate on minimizing random detection errors through appropriate peripheral matching circuits and improving sensor sensitivity via structural design modifications. The improvement of the device structure relies mainly on the optimization of the interdigital capacitance. The matching of microwave biosensors with electronic circuits and the introduction of algorithms can result in a miniaturized smart device.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. Ethical approval was not required for the studies on animals in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.

Author contributions

J-MZ: Writing–original draft, Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization. Y-KW: Writing–review and editing, Methodology, Software. B-WS: Writing–review and editing, Methodology. Y-XW: Writing–original draft, Validation. Y-FJ: Writing–review and editing, Supervision. G-LY: Writing–review and editing, Supervision. X-DG: Writing–review and editing, Supervision. TQ: Writing–review and editing, Supervision, Conceptualization, Funding acquisition, Project administration, Resources, Visualization.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research is supported by National Natural Science Foundation of China (Grant No. 61801146), Project funded by China Postdoctoral Science Foundation (Grant No. 2021M691284), Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No. SJCX23_1226), and Open Project of the Key Laboratory of Nanodevices and Applications, Chinese Academy of Sciences (Grant No. 22ZS07).

Acknowledgments

The authors acknowledge helpful conversations regarding the interpretation of these data with Prof. Xiaoman Zhou (School of Biotechnology, Jiangnan University, Wuxi, China). The sample of Figure 3 is designed by macrovector/Freepik.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Adan, A., Kiraz, Y., and Baran, Y. (2016). Cell proliferation and cytotoxicity assays. Curr. Pharm. Biotechnol. 17, 1213–1221. doi:10.2174/1389201017666160808160513

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Keywords: cytotoxicity assay, microwave sensors, live cells, drug concentrations, growth inhibition

Citation: Zhao J-M, Wang Y-K, Shi B-W, Wang Y-X, Jiang Y-F, Yang G-L, Gao X-D and Qiang T (2024) Microwave biosensor for the detection of growth inhibition of human liver cancer cells at different concentrations of chemotherapeutic drug. Front. Bioeng. Biotechnol. 12:1398189. doi: 10.3389/fbioe.2024.1398189

Received: 09 March 2024; Accepted: 23 April 2024; Published: 13 May 2024.

Reviewed by:

Copyright © 2024 Zhao, Wang, Shi, Wang, Jiang, Yang, Gao and Qiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiao-Dong Gao, [email protected] ; Tian Qiang, [email protected]

This article is part of the Research Topic

Insights in Biosensors and Biomolecular Electronics 2024: Novel Developments, Current Challenges, and Future Perspectives

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Please note you do not have access to teaching notes, topological optimization in 3d-magnetostatics: development of adjoint methods using the equations of magnetic moments.

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

ISSN : 0332-1649

Article publication date: 17 May 2024

The purpose of this paper is mainly to develop the adjoint method within the method of magnetic moment (MMM) and thus, to provide an efficient new way to solve topology optimization problems in magnetostatic to design 3D-magnetic circuits.

Design/methodology/approach

First, the MMM is recalled and the optimization design problem is reformulated as a partial derivative equation-constrained optimization problem where the constraint is the Maxwell equation in magnetostatic. From the Karush–Khun–Tucker optimality conditions, a new problem is derived which depends on a Lagrangian parameter. This problem is called the adjoint problem and the Lagrangian parameter is called the adjoint parameter. Thus, solving the direct and the adjoint problems, the values of the objective function as well as its gradient can be efficiently obtained. To obtain a topology optimization code, a semi isotropic material with penalization (SIMP) relaxed-penalization approach associated with an optimization based on gradient descent steps has been developed and used.

In this paper, the authors provide theoretical results which make it possible to compute the gradient via the continuous adjoint of the MMMs. A code was developed and it was validated by comparing it with a finite difference method. Thus, a topology optimization code associating this adjoint based gradient computations and SIMP penalization technique was developed and its efficiency was shown by solving a 3D design problem in magnetostatic.

Research limitations/implications

This research is limited to the design of systems in magnetostatic using the linearity of the materials. The simple examples, the authors provided, are just done to validate our theoretical results and some extensions of our topology optimization code have to be done to solve more interesting design cases.

Originality/value

The problem of design is a 3D magnetic circuit. The 2D optimization problems are well known and several methods of resolution have been introduced, but rare are the problems using the adjoint method in 3D. Moreover, the association with the MMMs has never been treated yet. The authors show in this paper that this association could provide gains in CPU time.

Optimization
Sensitivity analysis
Numerical methods
Method of magnetic moments
Volume integral method

Acknowledgements

Funding : This work was supported by ANR – French Research National Agency (ANR-21-CE05-0001-01).

Michel, S. , Messine, F. and Poirier, J.-R. (2024), "Topological optimization in 3D-magnetostatics: development of adjoint methods using the equations of magnetic moments", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/COMPEL-10-2023-0533

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Topological optimization in 3D-magnetostatics: development of adjoint
The purpose of this paper is mainly to develop the adjoint method within the method of magnetic moment (MMM) and thus, to provide an efficient new way to solve topology optimization problems in magnetostatic to design 3D-magnetic circuits.,First, the MMM is recalled and the optimization design problem is reformulated as a partial derivative ...

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Practical and ethical considerations when designing research

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Types of quantitative research designs

Types of qualitative research designs

Defining the population

Sampling methods

Case selection in qualitative research

Survey methods

Observation methods

Other methods of data collection

Secondary data

Operationalisation

Reliability and validity

Sampling procedures

Data management

Quantitative data analysis

Qualitative data analysis

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Shona McCombes

How to Write a Research Design – Guide with Examples

Step 1: Establish Priorities for Research Design

Step 2: Data Type you Need for Research

Primary Data Vs. Secondary Data

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Step 3: Data Collection Techniques

Step 4: Procedure of Data Analysis

Quantitative Data Analysis

Qualitative Data Analysis

Step 5: Write your Research Proposal

How to Write a Research Design – Conclusion

Frequently Asked Questions

How to write a research design?

How to write the design section of a research paper?

How to write a research design in methodology?

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