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Types of Research – Explained with Examples

DiscoverPhDs

  • By DiscoverPhDs
  • October 2, 2020

Types of Research Design

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

  • Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
  • Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

Methodology Research

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

Types of Research Methodology

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

Descriptive Research Design

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

Action Research Methods

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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  • Knowledge Base

Methodology

  • What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

  • Your overall research objectives and approach
  • Whether you’ll rely on primary research or secondary research
  • 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 objectives and that you use the right kind of analysis for your data.

Table of contents

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

  • Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

  • Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
  • 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

  • Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
  • Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

  • Your research questions and/or hypotheses
  • Your overall approach (e.g., qualitative or quantitative )
  • The type of design you’re using (e.g., a survey , experiment , or case study )
  • Your data collection methods (e.g., questionnaires , observations)
  • Your data collection procedures (e.g., operationalization , timing and data management)
  • Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Types of research papers

types of research in research paper

Analytical research paper

Argumentative or persuasive paper, definition paper, compare and contrast paper, cause and effect paper, interpretative paper, experimental research paper, survey research paper, frequently asked questions about the different types of research papers, related articles.

There are multiple different types of research papers. It is important to know which type of research paper is required for your assignment, as each type of research paper requires different preparation. Below is a list of the most common types of research papers.

➡️ Read more:  What is a research paper?

In an analytical research paper you:

  • pose a question
  • collect relevant data from other researchers
  • analyze their different viewpoints

You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic. It is important to stay neutral and not show your own negative or positive position on the matter.

The argumentative paper presents two sides of a controversial issue in one paper. It is aimed at getting the reader on the side of your point of view.

You should include and cite findings and arguments of different researchers on both sides of the issue, but then favor one side over the other and try to persuade the reader of your side. Your arguments should not be too emotional though, they still need to be supported with logical facts and statistical data.

Tip: Avoid expressing too much emotion in a persuasive paper.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information. You should include facts from a variety of sources, but leave those facts unanalyzed.

Compare and contrast papers are used to analyze the difference between two:

Make sure to sufficiently describe both sides in the paper, and then move on to comparing and contrasting both thesis and supporting one.

Cause and effect papers are usually the first types of research papers that high school and college students write. They trace probable or expected results from a specific action and answer the main questions "Why?" and "What?", which reflect effects and causes.

In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

An interpretative paper requires you to use knowledge that you have gained from a particular case study, for example a legal situation in law studies. You need to write the paper based on an established theoretical framework and use valid supporting data to back up your statement and conclusion.

This type of research paper basically describes a particular experiment in detail. It is common in fields like:

Experiments are aimed to explain a certain outcome or phenomenon with certain actions. You need to describe your experiment with supporting data and then analyze it sufficiently.

This research paper demands the conduction of a survey that includes asking questions to respondents. The conductor of the survey then collects all the information from the survey and analyzes it to present it in the research paper.

➡️ Ready to start your research paper? Take a look at our guide on how to start a research paper .

In an analytical research paper, you pose a question and then collect relevant data from other researchers to analyze their different viewpoints. You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information.

Cause and effect papers are usually the first types of research papers that high school and college students are confronted with. The answer questions like "Why?" and "What?", which reflect effects and causes. In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

This type of research paper describes a particular experiment in detail. It is common in fields like biology, chemistry or physics. Experiments are aimed to explain a certain outcome or phenomenon with certain actions.

types of research in research paper

  • How it works

Types of Research – Tips and Examples

Published by Carmen Troy at August 16th, 2021 , Revised On October 26, 2023

“Research is an investigation conducted to seek knowledge and find solutions to scientific and social problems.”

It includes the collection of information from various sources. New research can contribute to existing knowledge.

The types of research can be categorised from the following perspectives;

  • Application of the study
  • Aim of the research
  • Mode of inquiry
  • Research approach

Types of Research According to the Application Perspective

The different types of research, according to the application perspective, include the following.

Basic Research

Primary research is conducted to increase knowledge. It is also known as theoretical research, pure research, and fundamental research. It provides in-depth knowledge about the scientific and logical explanations and their conclusions.

The results of the primary research are used as the base of applied research. It is based on  experiments  and observation. The results of basic research are published in peer-reviewed journals.

  • What is global warming?
  • How did the Universe begin?
  • What do humans get stress?

Applied Research

Applied research is conducted to find solutions for practical problems. It uses the outcomes of basic research as its base. The results of applied research are applied immediately. It includes case studies, experimental research.

Example: Finding the solution to control air pollution.

Descriptive Research

Descriptive research  is carried out to describe current issues, programs, and provides information about the issue through  surveys  and various fact-finding methods.

It includes co-relational and comparative methods of research. It follows the Ex post facto research, which predicts the possible reasons behind the situation that has already occurred.

A researcher cannot control its variables and can report only about the current situation and its occurring.

Example: The widespread contaminated diseases in a specific area of the town. Investigation reveals that there is no trash removal system in that area. A researcher can hypothesise the reason that the improper trash removal system leads to the widespread of contaminated disease.

Analytical Research

In analytical research, a researcher can use the existing data, facts, and knowledge and critically analyses and evaluates the sources and material. It attempts to describe why a specific situation exists.

Example: Impact of video games on teenagers.

Explanatory

Explanatory research is conducted to know why and how two or more variables are interrelated. Researchers usually conduct experiments to know the effect of specific changes among two or more variables.

Example: A study to identify the impact of a nutritious diet on pregnant women.

Exploratory

Exploratory research is conducted to understand the nature of the problem. It does not focus on finding evidence or a conclusion of the problem. It studies the problem to explore the research in-depth and covers such topics which have not been studied before.

Example:  An investigation about the growing crimes against women in India.

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Types of Research According to the Mode of Inquiry

Qualitative research.

Qualitative research  is based on quality, and it looks in-depth at non-numerical data. It enables us to understand the comprehensive details of the problem. The researcher prepares open-ended questions to gather as much information as possible.

  • Stress level among men and women.
  • The obesity rate among teenagers.

Quantitative Research

Quantitative research is associated with the aspects of measurement, quantity, and extent. It follows the statistical, mathematical, and computational techniques in the form of numerical data such as percentages and statistics. The research is conducted on a large group of population.

  • Find out the weight of students of the fifth standard
  • Studying in government schools.

Types of Research According to the Research Approach

Longitudinal research.

Researchers collect the information at multiple points in time. Usually, a specific group of participants is selected and examined numerous times at various periods.

Example: If a researcher experiments on a group of women to find out the impact of a low carb diet within six months. The women’s weight and a health check-up will be done multiple times to get the evidence of the study.

Cross-Sectional Research

Cross-sectional research  gathers and compares the information from various groups of the population at the same point. It may not provide the exact reason and relationship between the subjects but gives a broad picture to study multiple groups at the same time.

Example: If a researcher wants to know the number of students studying in a school, he will get to know about the age groups, height, weight, and gender of the students at the same time.

Conceptual Research

It is associated with the concept and theory that describes the hypothesis being studied. It is based on  the inductive  approach of reasoning. It does not follow practical experiments. Philosophers, thinkers, logicians, and theorists use such research to discover new concepts and understand the existing knowledge.

Example: discoveries of Sir Isaac Newton and Einstein.

Empirical Research

It is also known as experimental research, which depends on observation and experience. It is based on the  deductive  approach of reasoning . A researcher focuses on gathering information about the facts, their sources and investigating the existing knowledge. Example: Is intermittent fasting the healthy weight loss option for women?

The researcher can come up with the result that a certain number of women lost their weight, and it improved their health. On the other hand, a certain number of women suffering from low blood pressure and diabetes didn’t lose weight, and they faced negative impacts of intermittent fasting on their health.

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Mixed Methods of Research

When you combine quantitative and qualitative methods of research, the resulting approach becomes mixed methods of research.

Over the last few decades, much of the research in the world of academia has been conducted using mixed methods. Due to its greater legitimacy, this particular technique has gained for several reasons, including the feeling that combining the two types of research can provide holistic and more dependable results.

Here is what mixed methods of research involve:

  • Interpreting and investigating the information gathered through quantitative and qualitative techniques.
  • There could be more than one stage of research. Depending on the topic of research, occasionally, it would be more appropriate to perform qualitative research in the first stage to figure out and investigate a problem to unveil key themes; and conduct quantitative research in stage two of the process for measuring relationships between the themes.

 Tips for Choosing the Right Type of Research

Choosing the right type of research is essential for producing relevant and actionable insights. The choice depends on your objectives, available resources, and the nature of the problem. Here are some tips to help you make the right decision:

Define your Research Objectives Clearly

  • Descriptive Research: To describe the characteristics of certain phenomena.
  • Exploratory Research: To explore a problem that hasn’t been studied in depth.
  • Explanatory (or Causal) Research: To explain patterns of cause and effect.
  • Predictive Research: To forecast future outcomes based on patterns.

Understand the Research Methods

  • Quantitative Research: Employs structured data collection (e.g., surveys) to generate statistical data.
  • Qualitative Research: Uses unstructured or semi-structured data collection methods (e.g., interviews, observations) to understand behaviour, motivations, etc.

Consider the Time Dimension

  • Cross-sectional Studies: Capture data at a single point in time.
  • Longitudinal Studies: Collect data over extended periods to observe changes.

Evaluate Available Resources

  • Budget: Some research methods, like experimental research, may require more funding.
  • Time: Exploratory or ethnographic studies may take longer than surveys.
  • Expertise: Ensure you or your team possess the skills needed for your chosen research method.

Consider the Nature of the Problem

Complex problems may require mixed-methods research (a combination of qualitative and quantitative).

Review Existing Literature

Review existing literature before settling on a type to see what methodologies were previously employed for similar questions.

Think about Data Collection

Consider the best method to gather data: surveys, interviews, experiments, observations, etc. Your choice affects the research type.

Ethical Considerations:

Ensure your chosen method abides by ethical standards, especially when human subjects are involved.

Generalisability Vs. Depth

Quantitative methods often allow for generalizability, while qualitative methods provide depth and detail.

Pilot Testing

If unsure, run a pilot study to test your chosen method’s feasibility and utility.

Stay Open to Adaptation

Sometimes, initial research can lead to unforeseen insights or complexities. Be prepared to adjust your approach if needed.

Seek Feedback

Discuss your research approach with colleagues, mentors, or experts in the field. They might offer valuable insights or identify potential pitfalls.

Stay Updated

Research methods evolve. Stay updated with the latest techniques, tools, and best practices in your field.

Frequently Asked Questions

What is research.

Research is a systematic inquiry aimed at discovering, interpreting, and revising knowledge about specific phenomena. It involves formulating hypotheses, collecting data, and analysing results to generate new insights or validate existing theories. Conducted in various fields, research can be empirical, theoretical, or experimental and is fundamental for informed decision-making.

What are the different Types of Research?

Different types of research include:

  • Descriptive: Describe and analyze phenomena.
  • Experimental: Manipulate variables to establish causation.
  • Correlational: Examine relationships between variables.
  • Qualitative: Gather insights and understanding.
  • Quantitative: Use numerical data for analysis.
  • Case study, survey, ethnography, and more.

What is research design?

Research design is a structured blueprint for conducting a study, outlining how data will be collected, analysed, and interpreted. It determines the overall strategy and approach to obtain valid, accurate, and reliable results. Research design encompasses choices about type (e.g., experimental, observational), method (qualitative, quantitative), and data collection procedures.

What is survey?

A survey is a research method used to gather data from a predefined group by asking specific questions. Surveys can be conducted using various mediums, such as face-to-face interviews, phone calls, or online questionnaires. They are valuable for collecting descriptive, quantitative, or qualitative information and gauging public opinion or behaviours.

What is research method?

A research method is a systematic approach used by researchers to gather, analyse, and interpret data relevant to their study. It dictates how information is collected and evaluated to answer specific research questions. Methods can be qualitative, quantitative, or mixed and include techniques like surveys, experiments, case studies, and interviews.

What is exploratory research?

Exploratory research is an initial study designed to clarify and define the nature of a problem. It’s used when researchers have a limited understanding of the topic. Instead of seeking definitive answers, it aims to identify patterns, ideas, or hypotheses. Methods often include literature reviews, qualitative interviews, and observational studies.

What is the purpose of research?

The purpose of research is to discover, interpret, or revise knowledge on specific topics or phenomena. It seeks to answer questions, validate theories, or find solutions to problems. Research enhances understanding, informs decision-making, guides policies, drives innovation, and contributes to academic, scientific, and societal advancement. It’s fundamental for evidence-based practices.

You May Also Like

What are the different research strategies you can use in your dissertation? Here are some guidelines to help you choose a research strategy that would make your research more credible.

Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.

Experimental research refers to the experiments conducted in the laboratory or under observation in controlled conditions. Here is all you need to know about experimental research.

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Research Design 101

Everything You Need To Get Started (With Examples)

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

Research design for qualitative and quantitative studies

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

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Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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types of research in research paper

Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

The four most common quantitative research design types are descriptive, correlational, experimental and quasi-experimental.

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

types of research in research paper

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

Case study design often involves investigating an individual to gain an in-depth understanding of their experiences, behaviours or outcomes.

How To Choose A Research Design

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

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

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

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

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

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

types of research in research paper

Recap: Key Takeaways

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

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

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

types of research in research paper

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This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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

Solly Khan

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

hetty

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

Belz

This was really helpful. thanks

Imur

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

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

Sam Msongole

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

Robyn Pritchard

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

kelebogile

how to cite this page

Peter

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

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

  • Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Shona McCombes

Shona McCombes

  • USC Libraries
  • Research Guides

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
  • 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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  • Indian J Anaesth
  • v.60(9); 2016 Sep

Types of studies and research design

Mukul chandra kapoor.

Department of Anesthesiology, Max Smart Super Specialty Hospital, New Delhi, India

Medical research has evolved, from individual expert described opinions and techniques, to scientifically designed methodology-based studies. Evidence-based medicine (EBM) was established to re-evaluate medical facts and remove various myths in clinical practice. Research methodology is now protocol based with predefined steps. Studies were classified based on the method of collection and evaluation of data. Clinical study methodology now needs to comply to strict ethical, moral, truth, and transparency standards, ensuring that no conflict of interest is involved. A medical research pyramid has been designed to grade the quality of evidence and help physicians determine the value of the research. Randomised controlled trials (RCTs) have become gold standards for quality research. EBM now scales systemic reviews and meta-analyses at a level higher than RCTs to overcome deficiencies in the randomised trials due to errors in methodology and analyses.

INTRODUCTION

Expert opinion, experience, and authoritarian judgement were the norm in clinical medical practice. At scientific meetings, one often heard senior professionals emphatically expressing ‘In my experience,…… what I have said is correct!’ In 1981, articles published by Sackett et al . introduced ‘critical appraisal’ as they felt a need to teach methods of understanding scientific literature and its application at the bedside.[ 1 ] To improve clinical outcomes, clinical expertise must be complemented by the best external evidence.[ 2 ] Conversely, without clinical expertise, good external evidence may be used inappropriately [ Figure 1 ]. Practice gets outdated, if not updated with current evidence, depriving the clientele of the best available therapy.

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Triad of evidence-based medicine

EVIDENCE-BASED MEDICINE

In 1971, in his book ‘Effectiveness and Efficiency’, Archibald Cochrane highlighted the lack of reliable evidence behind many accepted health-care interventions.[ 3 ] This triggered re-evaluation of many established ‘supposed’ scientific facts and awakened physicians to the need for evidence in medicine. Evidence-based medicine (EBM) thus evolved, which was defined as ‘the conscientious, explicit and judicious use of the current best evidence in making decisions about the care of individual patients.’[ 2 ]

The goal of EBM was scientific endowment to achieve consistency, efficiency, effectiveness, quality, safety, reduction in dilemma and limitation of idiosyncrasies in clinical practice.[ 4 ] EBM required the physician to diligently assess the therapy, make clinical adjustments using the best available external evidence, ensure awareness of current research and discover clinical pathways to ensure best patient outcomes.[ 5 ]

With widespread internet use, phenomenally large number of publications, training and media resources are available but determining the quality of this literature is difficult for a busy physician. Abstracts are available freely on the internet, but full-text articles require a subscription. To complicate issues, contradictory studies are published making decision-making difficult.[ 6 ] Publication bias, especially against negative studies, makes matters worse.

In 1993, the Cochrane Collaboration was founded by Ian Chalmers and others to create and disseminate up-to-date review of randomised controlled trials (RCTs) to help health-care professionals make informed decisions.[ 7 ] In 1995, the American College of Physicians and the British Medical Journal Publishing Group collaborated to publish the journal ‘Evidence-based medicine’, leading to the evolution of EBM in all spheres of medicine.

MEDICAL RESEARCH

Medical research needs to be conducted to increase knowledge about the human species, its social/natural environment and to combat disease/infirmity in humans. Research should be conducted in a manner conducive to and consistent with dignity and well-being of the participant; in a professional and transparent manner; and ensuring minimal risk.[ 8 ] Research thus must be subjected to careful evaluation at all stages, i.e., research design/experimentation; results and their implications; the objective of the research sought; anticipated benefits/dangers; potential uses/abuses of the experiment and its results; and on ensuring the safety of human life. Table 1 lists the principles any research should follow.[ 8 ]

General principles of medical research

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Types of study design

Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research and epidemiological research [ Figure 2 ]. Basic research includes fundamental research in fields shown in Figure 2 . In almost all studies, at least one independent variable is varied, whereas the effects on the dependent variables are investigated. Clinical studies include observational studies and interventional studies and are subclassified as in Figure 2 .

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Classification of types of medical research

Interventional clinical study is performed with the purpose of studying or demonstrating clinical or pharmacological properties of drugs/devices, their side effects and to establish their efficacy or safety. They also include studies in which surgical, physical or psychotherapeutic procedures are examined.[ 9 ] Studies on drugs/devices are subject to legal and ethical requirements including the Drug Controller General India (DCGI) directives. They require the approval of DCGI recognized Ethics Committee and must be performed in accordance with the rules of ‘Good Clinical Practice’.[ 10 ] Further details are available under ‘Methodology for research II’ section in this issue of IJA. In 2004, the World Health Organization advised registration of all clinical trials in a public registry. In India, the Clinical Trials Registry of India was launched in 2007 ( www.ctri.nic.in ). The International Committee of Medical Journal Editors (ICMJE) mandates its member journals to publish only registered trials.[ 11 ]

Observational clinical study is a study in which knowledge from treatment of persons with drugs is analysed using epidemiological methods. In these studies, the diagnosis, treatment and monitoring are performed exclusively according to medical practice and not according to a specified study protocol.[ 9 ] They are subclassified as per Figure 2 .

Epidemiological studies have two basic approaches, the interventional and observational. Clinicians are more familiar with interventional research, whereas epidemiologists usually perform observational research.

Interventional studies are experimental in character and are subdivided into field and group studies, for example, iodine supplementation of cooking salt to prevent hypothyroidism. Many interventions are unsuitable for RCTs, as the exposure may be harmful to the subjects.

Observational studies can be subdivided into cohort, case–control, cross-sectional and ecological studies.

  • Cohort studies are suited to detect connections between exposure and development of disease. They are normally prospective studies of two healthy groups of subjects observed over time, in which one group is exposed to a specific substance, whereas the other is not. The occurrence of the disease can be determined in the two groups. Cohort studies can also be retrospective
  • Case–control studies are retrospective analyses performed to establish the prevalence of a disease in two groups exposed to a factor or disease. The incidence rate cannot be calculated, and there is also a risk of selection bias and faulty recall.

Secondary research

Narrative review.

An expert senior author writes about a particular field, condition or treatment, including an overview, and this information is fortified by his experience. The article is in a narrative format. Its limitation is that one cannot tell whether recommendations are based on author's clinical experience, available literature and why some studies were given more emphasis. It can be biased, with selective citation of reports that reinforce the authors' views of a topic.[ 12 ]

Systematic review

Systematic reviews methodically and comprehensively identify studies focused on a specified topic, appraise their methodology, summate the results, identify key findings and reasons for differences across studies, and cite limitations of current knowledge.[ 13 ] They adhere to reproducible methods and recommended guidelines.[ 14 ] The methods used to compile data are explicit and transparent, allowing the reader to gauge the quality of the review and the potential for bias.[ 15 ]

A systematic review can be presented in text or graphic form. In graphic form, data of different trials can be plotted with the point estimate and 95% confidence interval for each study, presented on an individual line. A properly conducted systematic review presents the best available research evidence for a focused clinical question. The review team may obtain information, not available in the original reports, from the primary authors. This ensures that findings are consistent and generalisable across populations, environment, therapies and groups.[ 12 ] A systematic review attempts to reduce bias identification and studies selection for review, using a comprehensive search strategy and specifying inclusion criteria. The strength of a systematic review lies in the transparency of each phase and highlighting the merits of each decision made, while compiling information.

Meta-analysis

A review team compiles aggregate-level data in each primary study, and in some cases, data are solicited from each of the primary studies.[ 16 , 17 ] Although difficult to perform, individual patient meta-analyses offer advantages over aggregate-level analyses.[ 18 ] These mathematically pooled results are referred to as meta-analysis. Combining data from well-conducted primary studies provide a precise estimate of the “true effect.”[ 19 ] Pooling the samples of individual studies increases overall sample size, enhances statistical analysis power, reduces confidence interval and thereby improves statistical value.

The structured process of Cochrane Collaboration systematic reviews has contributed to the improvement of their quality. For the meta-analysis to be definitive, the primary RCTs should have been conducted methodically. When the existing studies have important scientific and methodological limitations, such as smaller sized samples, the systematic review may identify where gaps exist in the available literature.[ 20 ] RCTs and systematic review of several randomised trials are less likely to mislead us, and thereby help judge whether an intervention is better.[ 2 ] Practice guidelines supported by large RCTs and meta-analyses are considered as ‘gold standard’ in EBM. This issue of IJA is accompanied by an editorial on Importance of EBM on research and practice (Guyat and Sriganesh 471_16).[ 21 ] The EBM pyramid grading the value of different types of research studies is shown in Figure 3 .

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The evidence-based medicine pyramid

In the last decade, a number of studies and guidelines brought about path-breaking changes in anaesthesiology and critical care. Some guidelines such as the ‘Surviving Sepsis Guidelines-2004’[ 22 ] were later found to be flawed and biased. A number of large RCTs were rejected as their findings were erroneous. Another classic example is that of ENIGMA-I (Evaluation of Nitrous oxide In the Gas Mixture for Anaesthesia)[ 23 ] which implicated nitrous oxide for poor outcomes, but ENIGMA-II[ 24 , 25 ] conducted later, by the same investigators, declared it as safe. The rise and fall of the ‘tight glucose control’ regimen was similar.[ 26 ]

Although RCTs are considered ‘gold standard’ in research, their status is at crossroads today. RCTs have conflicting interests and thus must be evaluated with careful scrutiny. EBM can promote evidence reflected in RCTs and meta-analyses. However, it cannot promulgate evidence not reflected in RCTs. Flawed RCTs and meta-analyses may bring forth erroneous recommendations. EBM thus should not be restricted to RCTs and meta-analyses but must involve tracking down the best external evidence to answer our clinical questions.

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Conflicts of interest.

There are no conflicts of interest.

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Although research paper assignments may vary widely, there are essentially two basic types of research papers. These are argumentative and analytical .

Argumentative

In an argumentative research paper, a student both states the topic they will be exploring and immediately establishes the position they will argue regarding that topic in a thesis statement . This type of paper hopes to persuade its reader to adopt the view presented.

 Example : a paper that argues the merits of early exposure to reading for children would be an argumentative essay.

An analytical research paper states the topic that the writer will be exploring, usually in the form of a question, initially taking a neutral stance. The body of the paper will present multifaceted information and, ultimately, the writer will state their conclusion, based on the information that has unfolded throughout the course of the essay. This type of paper hopes to offer a well-supported critical analysis without necessarily persuading the reader to any particular way of thinking.

Example : a paper that explores the use of metaphor in one of Shakespeare's sonnets would be an example of an analytical essay.

*Please note that this LibGuide will primarily be concerning itself with argumentative or rhetorical research papers.

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What is an Academic Paper? Types and Elements 

types of academic papers

Written by students, early career scholars and researchers, an academic paper presents original research findings and case studies with the aim of contributing to the existing body of knowledge on a particular subject. Characterized by their rigorous and systematic approach to research, academic papers contribute to building a researcher’s reputation as an expert within their field, with the number of citations received serving as a measure of the impact that the researcher’s work has had. Unlike other forms of writing, academic papers demand a stringent adherence to specific formats, the use of formal language, and careful attention to detail. Typically, the information shared in academic papers is presented in well-defined sections like title and abstract, introduction, methodology, results, discussion, and conclusion. Many types of academic papers are employed for different situations and scopes. Let’s take a look at some of the different types of academic papers. 

Types of academic papers

Academic papers are differentiated based on the context of the paper, its length and structure, its purpose and who it addresses.  

  • Research papers  are the most common type of academic paper and present original research, usually conducted by PhD students who conduct in-depth investigations in their chosen field of study.  
  • Review papers, or literature reviews are academic papers that provide a comprehensive analysis and synthesis of existing research on a specific topic. They only assess existing literature on a subject and do not involve any empirical experiment. The methodology mentioned in review papers refers to the methods used to collect research.  
  • Case studies:   Researchers create this type of academic paper when they want to undertake and present their study on particular subjects, concepts, or incidents. Typically involving reasonably in-depth analysis of a topic, case studies can be beneficial for understanding certain historical events in recent times, such as market crashes or natural disasters, especially for future uses.  
  • Position papers:   Academic   papers that present an author’s stance on a particular issue or topic are called position papers. Researchers must present facts and evidence to support their views systematically. This kind of academic paper is commonly used in policy-making and legal professions.  
  • Conference papers:  These constitute a summary of any of the above types of academic papers to a length that can be appropriately discussed at a meeting or conference. Conference papers are usually presented when researchers want to introduce a new concept or gather insights from other experts on their work.  
  • Theoretical reports:  These are articles written by researchers who are working on formulating new theories based on existing research and provide an in-depth look at a specific topic based on existing literature and theoretical foundations. 

Elements of an academic paper

Research papers are different from fiction writing as they require rigorous citations, adherence to structure and appropriate styles to be accepted in academia. Every research paper has some key elements which make it identifiable as a research paper and make the theme of the paper clearly understood, along with the process involved with the said paper. As such, these rules must be adhered to while writing academic papers. Many publishing journals will have their guidelines, so be prepared to tweak your format in accordance with those guidelines. A typical format consists of the following key elements –  

  • Title and Abstract:  The title introduces the topic of the academic paper in a catchy, concise way, while the abstract gives us a summary of the whole paper. The abstract helps readers get an idea about the paper without having to read the entire paper.  
  • Introduction:  Usually placed at the start of an academic paper, the introduction enables researchers to better understand the topic of study. It highlights the research question, the scope of the research, its context, and its relevance. 
  • Methodology:  This section of the academic paper typically constitutes its main body. Researchers must provide a detailed, step-by-step account of the methodology followed to arrive at the findings. This section is important as it helps readers understand how you arrived at your conclusions and enables them to recreate the experiment—not just to verify the findings but also perhaps to build on it in the future.  
  • Results:  Typically placed towards the later part of an academic paper, the results section is where researchers can present their research findings in an accurate and detailed manner. Experts suggest using visual tools like graphs, tables and infographics when sharing numeric data and statistics. The results must be communicated in simple, clear, unambiguous language that readers can easily understand. 
  • Discussion:   Sometimes grouped with the results section, the discussion section is where research findings are discussed in detail. Researchers discuss the implications and limitations of their work and share the potential for further research.  
  • Conclusion:  The conclusion summarizes the entire academic paper, from the introduction and methodology to the results and discussion. It reinforces key messages and highlights important concepts and themes. 
  • References : This section of the academic paper lists the sources of information mentioned in the article as a bibliography so that the reader is able to refer to the sources. Ensuring accuracy in citations is imperative to avoid allegations of plagiarism, even if it was inadvertent. 

Different types of academic papers are employed based on the context of the paper, its length and structure, its purpose, and who it addresses. While each type of academic paper has its unique features, they all share a common set of critical elements that make them identifiable as research papers. By understanding and following these essential elements, researchers can effectively communicate their research findings and make meaningful contributions to their field of study.

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  • A Research Guide
  • Research Paper Guide

Different Types of Research Papers

  • According to the purpose
  • According to the depth of scope
  • According to the data type
  • According to variables manipulation
  • According to the type of inference
  • According to the time in which it is carried out
  • According to the sources of information
  • According to how the data is obtained
  • According to design
  • Other research paper types

Types of Research Papers

Types of research papers

As a way to make your journey through the research-type paper options a bit easier, let’s divide them by types of work.

According to the purpose:

  • Theoretical. Theoretical research type is one of the most popular types of research paper as it has a clear focus. If you have to work with this type, your main objective is to generate all currently available. Even if it has no practical appliance (like in Engineering or design), you must use it anyway. You must collect data and make sure that your target audience understands what your research is about and what theory it follows. Most of such research papers will relate to theories and basic analytical work.
  • Applied. This research type stands for something that can be approached scientifically based on practice. The aim here is to generate practical skills. It’s essential in Engineering, Healthcare, and Biology. For such types of papers, one can alternate between technological or scientific types of research, depending on your aims. A technological approach will be fitting if you wish to improve some processes. Now, the scientific research type would include prediction as you work with variables and design things.

According to the depth of scope:

  • Exploratory. It is most suitable for research type papers where you have to explore a not-well-known subject. Start with making a hypothesis and developing research. It can be an investigation talking about the role of video games in the development of teenagers.
  • Descriptive. This type of research is where you must describe certain characteristics or discuss specifics of some belief or an event. You may not have to research why something has caused these characteristic traits. You must describe and talk about how some things may change IF this or that takes place.
  • Explanatory. It’s one of the popular research methods since one has to analyze specific methodologies and help the target audience trace the cause-and-effect relations. It is close to descriptive writing by nature. Still, you must create a research environment since your findings may have to be re-created by others.
  • Correlational. This is where you identify the link between two or more variables. You must focus on determining whether certain research variables will be affected and see whether something is systematic regarding these changes (correlational research methodology).

According to the data type:

  • Qualitative . It’s used to collect, evaluate, and explain information based on obtained information. It means you have to approach a linguistic-semiotic method to things as you research. You can turn to analysis, interviews, questionnaires, and personal surveys. This is where statistical data helps! You must ask yourself “why” instead of “how.”
  • Quantitative. Such types of papers to write belong to one of the most challenging cases because quantitative stands for mathematical (think MATLAB) and computer-based software to check things. It also makes it possible to create a prognosis, which is why this type of research is usually met in engineering.
  • Mixed. It’s also possible to use both methodologies if you can support your research type assignment with source information and personal examples. If you are dealing with Psychology or Experimental study, use surveys and aid yourself with AI-based evaluation tools.

According to variables manipulation:

  • Experimental. Contrary to its title, you do not have to experiment per se. It’s about the design or replication of things you research. It means you have to re-create specific research conditions to discover what effects are caused by given variables. It’s where you primarily use case studies and sample groups.
  • Non-experimental. They often call this research type an observational study. It means that you have to provide analysis in its natural environment. You do not have to intervene in the process but consider turning to descriptive writing. This research may include observation of animals in their natural habitat or the use of the noise effect in the urban environment.
  • Quasi-experimental. These types of academic papers are not purely experimental, as you only work with two or three variables. Another aspect of this research is based on randomly chosen variables. It helps to decrease the bias in your study. It also helps to focus on relevant data and allows us to narrow things down.

According to the type of inference:

  • Deductive. It means the research is basically fixed since one has to focus on laws and things that can or cannot be. It helps to come to certain conclusions. As you look at the research problem, you use deduction to create your considerations. If you make assumptions and develop reliable evidence, this work method suits you.
  • Inductive. It’s one of the flexible methods to think about. The reason why it’s flexible is the way inductive research is generated. You conclude by observing and generalizing while different kinds of research occur. You have to collect data over a period, which makes the process less fixed.
  • Hypothetical-deductive approach. You have to make a hypothesis for your research work and use deduction methods to come up with a conclusion. The major difference is that a researcher also takes time to evaluate whether things are correct.

According to the time in which it is carried out:

  • Longitudinal. You might know this type of work as diachronic research. Despite the complex name, it focuses on the same issue or an event where a fixed period is taken. It has to track certain changes based on variables. It’s one of the most popular research papers in Healthcare, Nursing, Sociology, Psychology, and Education.
  • Cross-sectional. Also known as synchronous research, it is the type of work that approaches cross-sectional design. Here, you have to look at some event or a process at a certain point by taking notes. Thus, research can be used both for sample groups or when working with a case study.

According to the sources of information:

  • Primary. Most students are asked to use primary sources. It is exactly why we have a primary research paper method. The data must be collected directly (personal interviews, surveys, questionnaires, a field observation study, etc.) and represent first-hand information. It is perfect for papers in Psychology, Journalism, Healthcare, and subjects where accuracy is vital.
  • Secondary. This research type of work is mainly developed with sources that represent secondary references. These include books in print or found online, scientific journals, peer-reviewed documents, etc. If another expert or a student reviews a study, it is related to secondary research; so will your project.

According to how the data is obtained:

  • Documentary. As the name suggests, documentary research is based on the secondary references you used. It is a systematic review where you turn to secondary sources related to your subject of study. The most prominent types of research projects in this area are writing a literature review or working with a case study. It is one of the most accessible and clear types of research work.
  • Field. It is quite popular research these days as students tend to collect information in the field or at the location where something takes place. Think about researching Fashion Studies where you attend the shows or exploring Environmental Science, where you must observe a phenomenon and take notes.
  • Laboratory. The major difference in laboratory research type is working in a strictly-controlled environment where study notes are taken immediately. You must isolate unnecessary variables and use one or two scientific methods. Therefore, such type of research writing is called laboratory research. If your college professor asks for this assignment, consider keeping up with standards and rules.
  • Survey. This is where you have to work with the primary information or the use of first-hand data you obtain yourself. It is especially helpful when you work with a group to obtain variables. With this research type, you can also come up with certain conclusions to support your hypothesis and thesis statement.

According to design:

  • Fixed. When conducting a fixed research type, narrow things down and focus on temporal aspects. It means you have to discuss how often you will evaluate something, where your research will occur, a sample group, and other fixed variables. Working on fixed types of research reports, creating precise conditions, and follow strict protocols. Such research is related chiefly to lab reports or laboratory works mostly used in Healthcare and/or Law.
  • Flexible. Now, the flexible research type will provide you with a process where certain things will change as you take step after step in your research. The examples may include case studies where you have to observe the changes that may take over time. Another example would relate to Anthropology or Geography, where you have to observe a group of people or deal with a cross-cultural analysis. It can also relate to grounded-theory studies, where you should develop theoretical knowledge based on analysis and your thinking.

Other research paper types:

  • Argumentative. Also known as a persuasive research type paper, you have to persuade your target audience on your side and a point of view. You have to use at least one piece of evidence (references) to prove your point and support your argument. You must talk about different research opinions and show why your side is correct.
  • Analytical. Analytical research papers should always pose a problem and collect relevant information. You can look at another researcher’s works and provide an analysis based on various points of view. The main types of research papers include analysis and must keep the tone analytical and remain neutral without showing your thoughts unless only to guide the reader.
  • Definition. This research type requires describing the facts or arguments without using anything based on your opinion or an emotional constituent. You only have to offer information by including facts, yet let your data remain without analysis or bias.
  • Action-based. This research type assignment must conduct your work based on a process or a certain action causing things. It can also lead to social processes where a person’s actions have led to something. It can be some research about social movements and/or manufacturing processes.
  • Causal. It may relate to cause-and-effect papers where you must focus on the causes. This research type has to address the questions and explore the causes. It can be based on case studies related to business, education, environmental, educational issues, and more.
  • Classification. If you have to classify, compare, and contrast things, this method will be helpful. Start with the standards and the rules by setting your classification type immediately. Once you know it, your research paper will go smoothly.
  • Comparative. As a rule, this research will deal with comparative work where you take a methodology and compare two sample groups, two individuals, different beliefs, or situations. If you have to compare, discuss your objectives and then create two columns to determine differences and similarities.

What research paper type is most suitable for me?

It will always depend on the research paper objectives you wish to achieve. If you need clarification on the research type you must approach, consult your academic advisor or look closely at your grading rubric. If it says that you must develop an analytical study, it will require posing a specific research question or a problem. The next step would be to collect information on a topic and provide an analysis based on various points of view.

Likewise, if your grading rubric has the word “definition” mentioned, your research type paper must focus on the facts or argumentation. In this case, you should not provide your opinion or talk about what some author thinks. Only the definition of an object or belief is necessary.

As you can see, you only have to find out what your research must achieve. Set the purpose and look at the different types of research and possible methods to approach your problem . Once you know it, look at the research type papers and choose the most fitting option!

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Types of Research Papers.

Research papers play a crucial role in academia, allowing scholars to explore and contribute to the existing knowledge within their respective fields. However, not all research papers are created equal. There are various types of research papers, each serving a specific purpose and requiring distinct methodologies and writing styles. In this blog post, we will provide a comprehensive overview of the different types of research papers, shedding light on their characteristics, purposes, and key elements.

1. Descriptive Research Papers:  

Descriptive research papers aim to provide a detailed account or description of a particular phenomenon, event, or subject. These papers focus on answering questions related to “what” and “how.” Descriptive research papers often employ observational methods, surveys, or interviews to collect data. They are valuable in establishing a baseline understanding of a topic or providing an overview of existing conditions.

2. Experimental Research Papers: 

Experimental research papers involve conducting controlled experiments to investigate cause-and-effect relationships between variables. Researchers manipulate independent variables, measure dependent variables, and aim to establish causal relationships. These papers typically include sections such as hypothesis formulation, methodology, data analysis, and conclusion. Experimental research papers are common in scientific disciplines.

3. Analytical Research Papers: 

Analytical research papers emphasize critical thinking and analysis. They delve deep into a specific topic or issue, critically examining existing literature, theories, or concepts. These papers often require the author to evaluate different perspectives, present arguments, and provide evidence to support their claims. Analytical research papers contribute to the development of new theories or the refinement of existing ones.

4. Review Research Papers: 

Review research papers provide a comprehensive summary and evaluation of existing literature on a specific topic. They synthesize and analyze multiple sources to identify trends, gaps, or controversies in the field. Review papers help researchers gain a broader understanding of the current state of knowledge and identify areas that require further investigation. They are often published in academic journals and serve as valuable resources for scholars and students.

5. Argumentative Research Papers: 

Argumentative research papers aim to persuade the reader by presenting a clear argument or position on a specific issue. These papers require the author to gather evidence, present logical reasoning, and counter opposing viewpoints. Argumentative research papers are prevalent in disciplines such as philosophy, social sciences, and humanities, where different perspectives and debates are common.

6. Case Study Research Papers:  

Case study research papers provide an in-depth analysis of a particular individual, group, organization, or event. They involve detailed examination and interpretation of qualitative or quantitative data, often collected through interviews, observations, or document analysis. Case studies offer insights into complex phenomena and allow researchers to explore real-life contexts and unique scenarios.

7. Argumentative Research Papers:  

8. comparative research papers:  .

Comparative research papers involve the systematic comparison of two or more entities, such as countries, cultures, policies, or systems. These papers focus on identifying similarities, differences, and patterns to gain insights into the researched subjects. Comparative research papers can be qualitative or quantitative in nature, depending on the research objectives and methodology.

9. Historical Research Papers:  

Historical research papers examine past events, people, or periods to understand their impact on the present. These papers involve extensive archival research, analysis of primary and secondary sources, and interpretation of historical data. Historical research papers contribute to the understanding of historical contexts, social changes, and the evolution of societies.

10. Theoretical or Conceptual Research Papers:  

Theoretical or conceptual research papers aim to develop or refine theories, models, or frameworks within a particular field. These papers often involve proposing new concepts, exploring relationships between existing theories, or providing theoretical explanations for observed phenomena. Theoretical research papers contribute to advancing knowledge and understanding within a specific discipline.

11. Action Research Papers: 

Action research papers focus on addressing practical problems or challenges within a specific context. They involve collaboration between researchers and practitioners to develop and implement interventions, assess their effectiveness, and reflect on the outcomes. Action research papers emphasize the application of research findings to solve real-world problems and bring about positive change.

Conclusion.

Understanding the different types of research papers is essential for researchers and students alike. Each type serves a distinct purpose, requires specific methodologies, and follows unique writing styles. Whether it’s exploring a phenomenon, conducting experiments, analyzing existing literature, or presenting arguments, researchers must select the appropriate type of research paper to effectively communicate their findings and contribute to the knowledge base of their respective fields.

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Types of Research Papers

Jason Burrey

Table of Contents

Writing a research paper requires a special approach, depending on its type. Students associate completing this type of academic assignment with spending long hours on difficult writing. But writing academic work can be less challenging if you know how to distinguish different paper types. You will better understand what aspect to emphasize and how to present the information the right way. The paper type determines the tone of your work.

Let’s find what popular research work types and their main features to make your academic writing journey captivating and flawless are.

different types of research papers

What Is a Research Paper?

Before moving to paper-type details, let’s find out what research work is and how it differs from other written assignments. A research article is a form of academic writing providing analysis, evaluation, or interpretation of a topic based on empirical evidence. Research papers use statistical data and a strict code for citations. The structure of a research paper depends on assignment requirements. However, generally, it consists of:

  • Introduction
  • Literature review
  • Recommendations
  • Limitations
  • Acknowledgements
  • Figures and Tables

The language of your article should be formal, objective, hedged, and responsible. Plan and organize your writing carefully and precisely. It is required to use complex sentence structures and impersonal pronouns. When writing your research work, avoid wordiness, a vague thesis statement, informal language, description without analysis, and not citing sources. Use one style manual (MLA, APA, or Chicago) to cite them consistently.

Features of research articles are clear focus established by the thesis statement, straightforward structure, statements supported by evidence, and impersonal tone. The length of a research paper ranges from 4,000 to 6,000 words. However, depending on the assignment, your work can be 2,000 words or even 10,000 words. Your academic level and the assignment complexity influence the essay length.

Simple Steps for Writing Different Types of Research Papers

There are nine simple steps you should follow if you wonder: “how can I write my research paper ?”

  • Carefully read the assignment guidelines.
  • Select an engaging article topic.
  • Do early research.
  • Create a powerful thesis statement.
  • Find reliable sources.
  • Write a paper outline.
  • Create an essay draft.
  • Follow citation and formatting rules.
  • Thoroughly edit and proofread your work.

When you need research paper help for some reason, you can find a lot of professional writing services and buy research work.

Different Types of Research Article

There are seven main research work types. Explore them to know what approach to take to create a high-quality paper in the future. Here you can find each type’s specifics and differences to prepare for your assignment the best way. If you have some issues with task completion, choose a reliable service and buy a research paper.

#1 Argumentative Research Papers

Creating an argumentative paper requires a writer to present arguments related to the topic from different points of view. They should analyze the two sides and propose their pros and cons. After that, an author should choose one viewpoint and prove its correctness using evidence from primary sources. There is a special argumentative paper structure that is aimed at persuading the reader to support the writer’s opinion. Thus, describe the problem from two different viewpoints, suggest their pros and cons, and give preference to one.

#2 Analytical Research Papers

It may seem challenging to write an analytical work, but once you find its features, structure, and guidelines, there’s nothing to worry about. A writer should analyze in their paper ideas, facts, events, or issues. It requires an objective analysis and critical thinking to provide strong arguments. You should not take any viewpoint and neutrally describe every point supporting them with relevant information. The analytical paper is based on describing multiple points of view, analyzing all points, and drawing a general conclusion.

#3 Cause and Effect Research Papers

These papers are created to find what is the cause of the expected result. Students without much writing experience are generally assigned to complete such research works. In their papers, they have to describe a situation, present effects, and causes, and draw a conclusion. But this paper type is not as simple as it seems at first sight. Depending on your academic level and subject, a professor may ask you to determine the possible result if conditions change.

#4 Problem-Solution Research Papers

Dealing with this paper type, a writer should describe the problem, present their solution to it, and prove why it is correct. Your task is to find a relevant issue that will be interesting to solve and to engage the readers to explore your solution. Provide reliable data to support your opinion. Consider adding some examples, statistics, and data.

#5 Experimental Research Papers

If you study biology, physics, chemistry, or sociology, this paper type is right for you. When creating an experimental work, a writer should describe their experimental process. This paper provides useful experience and relevant data. Conclude the paper proving that your experiment makes a great contribution to the field.

#6 Report Research Papers

A report paper provides a logical and detailed summary of a case study. A researcher outlines what has been done for the research. The paper includes information, data characteristics, and necessary facts to summarize the findings.

#7 An Interpretive Essay

Such essays are assigned to social science and literature students to show their theoretical knowledge of the subject. Interpret someone’s piece of writing and identify their methods. It is required to support the thesis statement and findings with relevant data.

Types of a Research Article

Research articles are often associated with research articles, and there is no difference between them. Some scholars suggest that works are longer and more detailed. So let’s see what six types of research articles are:

  • The original research article is a manuscript for a journal.
  • A review article is a comprehensive research summary consisting of a systematic review, literature review, and meta-analysis.
  • Short communications are a type of research article that provides a summary of research data.
  • A book chapter is a separate section of a book.
  • The book review is a brief report of a book consisting of an introduction, author profile, book format, and content.
  • Conference materials are article types that can be presented as conference abstracts, posters, and presentation extracts.

Research Paper Styles

If you need research work help, check out the main research article styles. Educational institutions worldwide require their students to adhere to one of the following paper formats or styles:

American Medical Association (AMA) Style

AMA style is commonly used in medical publications. It has a special citation format with in-text references cited numerically in consecutive order using Arabic numerals. Double-space and 12-point font are preferable.

Associated Press Style

The AP style is used mainly for writing news. It’s characterized by consistency, logic, and brevity. Writers avoid offensive and stereotypical language in their works. This style is essential for print journalism.

Chicago Manual of Style

Chicago format style is used in history, physical, natural, and social sciences by many writers and scientists. It is crucial to know about this style that the note numbers are placed within the text, and the sources are found at the end of the chapter.

American Psychological Association (APA) Style

This format is one of the most widely used in academic writing. Students benefit from the APA style frequently in the social sciences. It is also easy to read due to its 12-point font. There are many features of this style, but you should remember that it differs by a left-aligned running header with the title of your study on each page.

Modern Language Association (MLA) Style

MLA style is one of two of the most popular article formats. It is widely used for writing papers on humanities, literature, and English. This style is simple and easy; just use double-spaced throughout the paper and a 12-point font.

Can Research Papers Have Opinions?

Giving your opinion on the issue presupposes subjective evaluation, and in the article, we found that a research paper should be written in an impersonal, objective tone. That’s a controversial question, and we’ll try to handle it.

You can include opinions of prominent scholars in the field and give citations and references to their works. An author should show in their work that they have a personal view on the question and can substantiate it by research and literature. Persuade readers in your paper that your opinion is worth considering using arguments.

Besides, there is an opinion research paper type that aims at presenting the writer’s opinion on a specific topic. Here you are welcome to express your viewpoint but support it with reliable sources and documents.

Writing a research paper seems an insurmountable task with many aspects to consider. Determine your paper type, and your writing process will be much easier as you will have special guidelines.

Contact a research paper writer if you require academic assistance.

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  • Radka Nacheva   ORCID: orcid.org/0000-0003-3946-2416 1 ,
  • Maciej Czaplewski   ORCID: orcid.org/0000-0003-1888-8776 2 &
  • Pavel Petrov   ORCID: orcid.org/0000-0002-1284-2606 1  

Research classification is an important aspect of conducting research projects because it allows researchers to efficiently identify papers that are in line with the latest research in each field and relevant to projects. There are different approaches to the classification of research papers, such as subject-based, methodology-based, text-based, and machine learning-based. Each approach has its advantages and disadvantages, and the choice of classification method depends on the specific research question and available data. The classification of scientific literature helps to better organize and structure the vast amount of information and knowledge generated in scientific research. It enables researchers and other interested parties to access relevant information in a fast and efficient manner. Classification methods allow easier and more accurate extraction of scientific knowledge to be used as a basis for scientific research in each subject area. In this regard, this paper aims to propose a research classification model using data mining methods and techniques. To test the model, we selected scientific articles on digital workplace accessibility for the disabled retrieved from Scopus and Web of Science repositories. We believe that the classification model is universal and can be applied in other scientific fields.

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The project "Impact of digitalization on innovative approaches in human resources management" is implemented by the University of Economics—Varna, in the period 2022–2025. The authors express their gratitude to the Bulgarian Scientific Research Fund, Ministry of Education and Science of Bulgaria for the support provided in the implementation of the project "Impact of digitalization on innovative approaches in human resources management," Grant No. BG-175467353-2022-04/12-12-2022, contract No. KP-06-H-65/4 – 2022.

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Nacheva, R., Czaplewski, M. & Petrov, P. Data mining model for scientific research classification: the case of digital workplace accessibility. Decision (2024). https://doi.org/10.1007/s40622-024-00378-z

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  • Published: 11 September 2023

Analysis of the research progress on the deposition and drift of spray droplets by plant protection UAVs

  • Qin Weicai 1 , 2 &
  • Chen Panyang 3  

Scientific Reports volume  13 , Article number:  14935 ( 2023 ) Cite this article

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  • Plant sciences

Plant protection unmanned aerial vehicles (UAVs), which are highly adapted to terrain and capable of efficient low-altitude spraying, will be extensively used in agricultural production. In this paper, single or several independent factors influencing the deposition characteristics of droplets sprayed by plant protection UAVs, as well as the experimental methods and related mathematical analysis models used to study droplet deposition and drift, are systematically investigated. A research method based on farmland environmental factors is proposed to simulate the deposition and drift characteristics of spray droplets. Moreover, the impacts of multiple factors on the droplet deposition characteristics are further studied by using an indoor simulation test system for the spraying flow field of plant protection UAVs to simulate the plant protection UAVs spraying flow field, temperature, humidity and natural wind. By integrating the operation parameters, environmental conditions, crop canopy characteristics and rotor airflow, the main effects and interactive effects of the factors influencing the deposition of spray droplets can be explored. A mathematical model that can reflect the internal relations of multiple factors and evaluate and analyze the droplet deposition characteristics is established. A scientific and effective method for determining the optimal spray droplet deposition is also proposed. In addition, this research method can provide a necessary scientific basis for the formulation of operating standards for plant protection UAVs, inspection and evaluation of operating tools at the same scale, and the improvement and upgrading of spraying systems.

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Introduction

In agriculture, aerial spray is widely used to spray fertilizers, herbicides, fungicides and other materials used for crop protection 1 . Compared with large fixed-wing agricultural aircraft, small unmanned aerial vehicles (UAVs) are particularly advantageous because they are highly maneuverable and do not need any airport for taking off or landing 2 . In recent years, aerial machinery for plant protection, especially aerial spray by small plant protection UAVs, has developed rapidly 3 . Small plant protection UAVs have greater application prospects in agricultural production because of their better terrain adaptability and low-altitude spraying capability (Figs. 1 and 2 ) 4 , 5 , 6 , 7 . However, as an emerging technology, UAV spraying technology in agricultural pest control are not common due to the lack of operational standards and uncertainty about the best spraying parameters, which leads to a series of problems, such as the poor uniformity of droplet deposition distribution and low levels of fog deposition.

figure 1

Single-rotor UAV spraying.

figure 2

Multirotor UAV spraying.

Some studies have shown that if the aerial spraying parameters are not set scientifically, it will lead to not only repeated spraying and missed spraying, degrading the effect of pest control but also pesticide drift 8 . The use of new pesticide additives and the innovative research and development of precise spraying equipment of plant protection UAVs along with its safe and efficient use in the prevention and control of diseases, pests and weeds are indispensable means to increase the pesticide deposition amount and reduce drift. Studying the deposition characteristics of spray droplets is not only of scientific significance for the development of new pesticide formulations and precise spraying equipment of plant protection UAVs but also of practical guiding significance for the safe and efficient use of pesticides in farmland. Due to many factors, such as the natural environment, pesticide characteristics, crop canopy characteristics, and plant protection UAV operating parameters, it is a complicated process to study the uniformity and penetration of spray droplets. To improve the spraying effect and reduce drift, scientific and technological staff all over the world have carried out a large number of exploratory studies on the deposition and drift characteristics of spray droplets through field or wind tunnel experiments or mathematical model analysis 9 , 10 , 11 , 12 , 13 . The main factors and secondary factors influencing the characteristics of droplet deposition and drift are organized from the many influencing factors (nozzle, droplet, aircraft type, weather factors, etc.), and the functional relationship between the amount of different droplet deposition and drift and their influencing factors are determined. However, there are not sufficient deposition models for plant protection rotor UAVs, and the existing models consider only a few influencing factors, which need to be further modified.

With the development of UAV technology, there are an increasing number of studies on the droplet deposition rules, operation parameter optimization and evaluation methods of pesticides applied by plant protection UAVs in rice fields and maize fields 14 , 15 , 16 , 17 ; however, these studies have defects in that the meteorological factors in the farmland environment are unstable and uncontrollable, the UAV track easily deviates, resulting in the poor uniformity of droplet deposition distribution (the coefficient of variation may be above 40% 16 , while it is usually below 10% for spraying by ground equipment), the test result cannot be well repeated, and different types of UAVs cannot be easily evaluated at the same scale. Thus, it is difficult to evaluate the droplet deposition characteristics of different types of UAVs scientifically. Some research has established mathematical models to study the impact of plant protection UAV operating parameters (operating height, operating speed, and spraying flow rate) on droplet deposition and drift characteristics 18 , 19 , 20 and determined the main effects influencing droplet deposition. However, due to the lack of conformity between the assumptions of these models and farmland practice, they neglected the influence of the characteristics of the crop canopy and the interaction of multiple factors such as the environment, crops, and operating parameters of application equipment on the droplet deposition characteristics (uniformity of distribution and penetration), making the results obtained through analysis with existing mathematical models highly deviate from practice.

In this paper, the current status and problems of research on the deposition and drift of spray droplets from plant protection drones are introduced, and the importance of research in this area to improve the effectiveness of pesticide application and reduce drift hazards is emphasized. The need for more in depth, comprehensive and systematic research on the deposition and drift of spray droplets from plant protection drones is highlighted, and the problems and challenges of the current research are pointed out, providing important guidance and references for future research.

Research on the influencing factors of spray droplet deposition characteristics

Studying droplet deposition characteristics (uniformity and penetration) is always a major subject in pesticide application technology research 21 . The deposition characteristics of spray droplets are influenced by application techniques and equipment, crops, the environment, etc. Detailed influencing factors include the wind speed, wind direction, leaf area index, target crop canopy structure, leaf inclination, leaf surface characteristics, and characteristics of the spray droplet population (release height, release rate, application liquid volume, spray droplet particle size spectrum) 22 , 23 , 24 .

Several studies have investigated the influence of various factors on droplet deposition characteristics in plant protection UAV spraying. Diepenbrock noted that plant leaf characteristics, such as size, inclination angle, drooping degree, and spatial arrangement, impact the composition quantity and distribution quality within the crop canopy structure, subsequently affecting droplet penetration and deposition 25 . Song et al. found that altering the initial velocity of droplets increases deposition amounts on horizontal and vertical targets. Factors like flying altitude and speed of different aircraft types have been extensively studied for their influence on droplet deposition and drift 26 . Qiu et al. used an orthogonal experimental method to study the deposition distribution rules of droplets sprayed by unmanned helicopters at different flying heights and speeds under field conditions. They established a relationship model that clarifies the interactions between deposition concentration, uniformity, flying speed, and flying height, providing valuable insights for optimizing spray operation parameters 18 . Chen et al. investigated the pattern of aerial spray droplet deposition in the rice canopy using a small unmanned helicopter. They explored the effects of different spraying parameters on droplet distribution, specifically analyzing the deposition of growth regulator spraying 27 . Wang et al. proposed a method for testing the spatial mass balance of UAV-applied droplets and conducted field experiments on three types of UAVs to accurately determine the spatial distribution of the droplets and the downdraft field. They also conducted an experimental study on the droplet deposition pattern of hovering UAV variable spraying and highlighted the significant impact of downward swirling airflow on droplet deposition distribution 14 . Qin et al. focused on the influence of spraying parameters, such as operation height and velocity, of the UAV on droplet deposition on the rice canopy and protection efficacy against plant hoppers, using water-sensitive paper to collect droplets and statistically analyzing their coverage rates. The findings indicated that UAV spraying exhibited a low-volume and highly concentrated spray pattern 19 .

In summary, there are many factors influencing the deposition characteristics (uniformity and penetration) of spray droplets. However, in most of the current research on spraying by plant protection UAVs, only the influence of factors such as the flying height and flying speed on droplet deposition in the field environment is taken into consideration. Considering the influence of the interaction between environmental factors, crop canopy characteristics (growth stage, leaf area index, leaf inclination angle) and plant protection UAV spraying parameters on droplet deposition characteristics, there is neither in-depth understanding nor relevant reports, especially under controllable environmental conditions (Fig.  3 ). To promote high-efficiency spraying technology for plant protection UAVs, targeted basic research should be carried out on the analysis of the influencing factors of plant protection UAV spraying and the optimal deposition of droplets.

figure 3

Description of the deposition and drift with rotor UAV spraying.

Research on the experimental means and testing methods of droplet deposition and drift

At present, the deposition and drift of droplets are mainly researched by field tests and wind tunnel tests 28 , 29 , 30 , 31 , 32 . Field test research on pesticide deposition and drift is similar to the actual situation, but it is quite difficult to acquire correct data due to the constant changes in meteorological factors such as the wind speed, wind direction, temperature and humidity. In addition, Emilia et al. noted that the terrain and plant morphology also influence the wind flow and droplet deposition, leading to considerable deviation among repeated test results 33 . Therefore, it is difficult to accurately determine the total amount and distribution of pesticides drifting in the air 34 . The wind tunnel laboratory can provide a controllable environment to simulate the external spraying conditions, and the wind speed and direction can be easily controlled. Therefore, it is an important means to study the drift characteristics of spraying components and avoid many defects in field test research 10 , 35 . The typical wind tunnels that are widely used in agricultural aviation spraying technology are shown in Table 1 36 , 37 .

Internationally well-known professional research institutions for pesticide application, such as the Julius Kuehn Institute-Federal Research Centre for Cultivated Plants (JKI, formerly BBA) and USDA-Agricultural Research Service, Application Technology Research Unit (USDA-ARS-ATRU), have a circular closed low-speed standard wind tunnel (Fig.  4 ). This wind tunnels are widely used to assess the distribution, degradation and drift of pesticide sprays, simulating real crop and environmental conditions. The advantages are that they provide accurate measurements of pesticide distribution and drift and are able to reproduce wind field conditions in realistic environments. However, circular low-speed wind tunnels have limitations when it comes to parameters such as spray particle size, density and flow rate for different pesticides. The Silsoe Research Institute, UK (SRI) has a standard linear low-speed wind tunnel. This wind tunnel can be used to test the performance of agricultural mechanised sprayers and the design of sprayers. The advantage is that they can simulate actual operating conditions and can accurately test the performance and flow rate of agricultural mechanised sprayers. However, linear low speed wind tunnels are typically more expensive than circular wind tunnels and can only simulate a single environmental condition. The Center for Pesticide Application and Safety (CPAS) of the University of Queensland in Australia has an open-path wind tunnel (Fig.  5 ). This type of wind tunnel can be used to test aspects such as drift and particle distribution of agricultural sprayers. The advantages are ease of operation, low cost and the ability to reproduce wind fields under different environmental conditions. However, open path wind tunnels do not simulate realistic crop environments and have unstable wind speeds. In 2014, the Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, built the NJS-1 plant protection direct flow closed wind tunnel (Fig.  6 ). This type of wind tunnel is mainly used to evaluate different sprayers in terms of performance and droplet distribution. The advantages are the ability to simulate a realistic farm environment with high accuracy and the ability to test different types and brands of sprayers. However, straight-through enclosed wind tunnels are only suitable for small equipment and small-scale trials and are costly. In 2018, the National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology of South China Agricultural University built a high- and low-speed composite wind tunnel for agricultural aviation research (Fig.  7 ). This wind tunnel is suitable for agricultural aerial research and can simulate the effects of spraying at different heights and wind speeds. The advantage is that it can accurately test the effects of pesticide spraying at different heights and speeds, and can improve the efficiency and accuracy of agricultural aerial spraying. However, high and low speed composite wind tunnels are relatively costly and require a high level of technology and equipment requirements. As the basic conditions for technical research, these wind tunnels have made great contributions to the study of pesticide deposition and drift rules, product testing, and product optimization 38 , 39 , 40 , 41 , 42 . However, for the study of spray droplet deposition and drift under the disturbance of the wind field of plant protection UAVs, the single-direction wind tunnel simulation test is still insufficient to simulate the combined effect of the downward swirl flow under the rotor and the natural wind. In addition, the existing agricultural wind tunnels are limited in size, so plant protection UAVs cannot be placed. In the military, a scaled model method is used to put UAVs into wind tunnels for research 43 , 44 , but it is not suitable for research on pesticide spraying with plant protection UAVs, and the airflow will rebound from the tunnel wall.

figure 4

Circle closed low-speed wind tunnel.

figure 5

Open wind tunnel.

figure 6

NJS-1DC closed wind tunnel.

figure 7

High and close speed composite wind.

Another important test technique for drift research is the sampling and analysis of droplet drift. Test studies on the drift of aerial mist in developed countries such as the United States and Germany are carried out with advanced test instruments, including automatic air samplers, gas or liquid chromatography, fluorescence analyzers, and electronic scanners. to collect and analyze the droplet deposition amount, the number of droplets, the coverage density of droplets, and the content of substances and study the correlation between additive concentration, spraying height and drift 4 , 45 , 46 . However, these traditional methods involve a long collecting and processing cycle, samples have to be processed in the lab, and it is difficult to express the dynamics of droplets in air. Particle image velocimetry (PIV) and LIDAR scanning test methods can solve the above problems, and each has its own advantages. PIV can obtain the three-dimensional spatial velocity vector of droplets and droplet size with a high sampling accuracy but limited spatial measurement scale 47 , 48 , 49 ; the LIDAR scanning method, realized by layered scanning, can quickly and accurately obtain the large-scale spatial droplet point cloud data and inversely form the three-dimensional distribution and temporal-spatial change of the droplets, but cannot reflect the spatial velocity vector change of the droplets 50 . The advantages, disadvantages and applications of droplet deposition and drift measurement methods are shown in Table 2 51 .

Overall, the sampling and analysis of droplet drift, along with techniques such as PIV and LIDAR scanning, play a crucial role in studying and understanding the behavior of droplets during aerial spraying. These methods provide valuable insights into droplet deposition, drift patterns, and the effects of various factors, enabling researchers to optimize spray practices, minimize drift, and enhance the efficiency and effectiveness of plant protection UAV applications.

Research on the mathematical analysis model of spray droplet deposition characteristics

In the development of spraying equipment and the determination of the optimal deposition conditions for spray, a large amount of data and information are needed to explain the influence of different factors on the spraying performance and the relationship between variables. At present, spraying drift modeling can be divided into models based on mechanics and models based on statistics 52 , 53 , 54 .

One of the models based on mechanics analyzes the movement of a single droplet in the airflow field by the Lagrangian trajectory tracking analysis method. Teske et al. established the AGDISP model by the analytical Lagrangian method to describe aerial spraying under the condition of ignoring the influence of aircraft wake and atmospheric turbulence 46 . This model takes not only the aircraft type, environmental conditions, and droplet properties but also the influencing factors of the nozzle model into consideration. The user can input the parameters of the nozzle, droplet spectrum, aircraft type and weather factors. from an internal database and predict the drift potential. It can effectively and accurately predict a range of 20 km but is generally used for fixed-wing aircraft. Duga et al. and Gregorio et al. also studied the deposition distribution of aerial spray in orchards with the Lagrangian discrete phase model, and the result of the numerical model showed that the prediction error of total deposition on the fruit tree canopy is above 30% 48 , 51 . Dorr et al. developed a spray deposition model for whole plants based on L-studio, which takes into account the plant leaf wettability, impact angle, droplet break-up and rebound behavior, and the number of sub-droplets produced 55 . In 2020, Zabkiewicz et al. used an updated version of the software based on this model, developing a new user interface and refining the droplet fragmentation model 56 .

Another model based on mechanics is realized with the CFD (Computational Fluid Dynamics) method 57 , 58 , but there are still large errors between the simulated value and the real value of some models due to various factors. Holterman et al. carried out a series of cross-wind single nozzle field experiments in consideration of the traveling speed, entrained airflow, geometric parameters of the farmland, sprayer system setting parameters and environmental factors when studying the droplet deposition drift model of ground boom sprayers to calibrate the mathematical model. The results showed that when the height from the crop canopy is less than or equal to 0.7 m, the error between the test and the model simulation is within 10%, but the error between droplet deposition and drift prediction gradually increases as the height of the spray boom increases 59 , 60 , 61 .

Chinese scientific and technological staff have conducted experimental research and numerical analysis on the numerical simulation and mathematical modeling of spraying droplet deposition and drift prediction of ground plant protection equipment and have drawn some conclusions that physical quantities such as the operating speed, droplet size and crosswind impact the droplet deposition and drift process (Figs. 8 and 9 ) 62 , 63 . Zhu et al. developed the DRIFTSIM based on CFD and Lagrangian methods with a CFD simulation database for ground drift prediction and a user interface to access drift-related data 64 . Hong et al. constructed an integrated computational hydrodynamic model to predict the deposition and transport of pesticide sprays under the canopy in apple orchards during different growth periods 65 .

figure 8

Rotor wind field test platform based on a wind tunnel.

figure 9

Layout scene of droplet drift.

The above research proves that computer simulation technologies are widely applicable to the prediction research of droplet deposition under various complicated wind-supply airflow conditions 66 . The existing AGDISP model is relatively developed and only suitable for research on fixed-wing aircraft, which is very different from research on plant protection UAVs. The current plant protection UAV spraying prediction model still has problems such as large relative errors between the experimental value and simulation value of the deposition and drift at each measurement point. Therefore, the prediction accuracy of the numerical model for the spray droplet deposition of plant protection UAVs is still low and needs to be improved, and there is a lack of in-depth basic research on analyzing the rotor flow field and establishing mathematical analysis models for droplet deposition 67 .

The rotor wind field test platform and droplet drift

The use of UAVs for crop spraying has become increasingly popular due to its efficiency and effectiveness. However, accurately analyzing the spraying process is challenging due to the complex flow field of the droplets in the air and the multitude of factors that can affect their deposition characteristics. Current testing systems rely on simple methods such as static targets or trays, which do not accurately represent the dynamic and complex nature of the real environment. To better study the UAV spraying flow field, a corresponding indoor simulation test system is needed. The indoor simulation system proposed in this study combines a natural wind simulation system and a rotor simulation system that can simulate several factors present in the natural environment that affect droplet deposition characteristics. The natural wind simulation system can effectively replicate wind speed variations, which is a key factor influencing droplet dispersion and deposition. By adjusting the settings of the wind simulation system, it is possible to replicate a range of wind speeds encountered in the field, allowing researchers to study the effects of different wind speeds on droplet behaviour and deposition. By adjusting the settings of the rotor simulation system, it is possible to demonstrate the magnitude of the downwash airflow at different speeds of the UAV rotor. However, it is important to note that while wind speed variations can be simulated, other factors, such as wind direction and turbulence, may have limitations in being accurately replicated in an indoor simulation system. These factors may require further development of simulation techniques to achieve more accurate replication. Nevertheless, the inclusion of natural wind simulation systems and rotor simulation systems in indoor simulation setups provides a valuable tool for studying the effects of wind speed.

The fluorescence tracer method involves adding a fluorescent dye or tracer to the liquid spray mixture used in the UAV spraying process. When these droplets containing the fluorescent tracer are released into the air, they can be illuminated with a specific wavelength of light, typically ultraviolet (UV) light. The fluorescent dye absorbs this UV light and re-emits it at a longer wavelength, usually in the visible range.

The high-speed camera is synchronized with the UV light source and captures the emitted fluorescent signals from the droplets. By analyzing the recorded video footage, researchers can precisely track the movement and behavior of the fluorescent droplets in the air. The high-speed camera captures images at a rapid frame rate, allowing for the visualization and analysis of the droplet flow field in detail.

The proposed indoor simulation test system for the spraying flow field of plant protection UAVs is a comprehensive and innovative method that combines the fluorescence tracer method and high-speed camera method to accurately track the dynamic changes in the local droplet flow field in the air. This system also includes a natural wind simulation system, which allows for the more realistic simulation of the actual environment, and thus more accurately reproduces the complex factors that affect droplet deposition characteristics. This method represents a significant improvement over existing testing systems, as it provides a more accurate and comprehensive analysis of the deposition process of droplets affected by multiple factors, enabling researchers to more effectively study the flow field and optimize the spraying process for plant protection UAVs. Overall, this proposed system has the potential to revolutionize the study of UAV spraying flow fields and could lead to significant advancements in the field of plant protection. Therefore, the method proposed in this paper is superior to the methods currently in use (Fig.  10 ).

figure 10

Diagram of the rotor wind field test platform and droplet drift.

In conclusion, existing studies on plant protection UAV spraying have primarily focused on isolated factors, such as flying height, flying speed, and nozzle flow, without considering the interaction effects among other influential factors. This limitation calls for the need to conduct experimental research that combines spray droplet deposition characteristics with crop canopy characteristics in a controllable environment, encompassing environmental conditions and operating parameters. The proposed research aims to address this gap by developing an indoor simulation system that incorporates a natural wind simulation system. This innovative system enables the study of droplet deposition characteristics influenced by multiple factors in a realistic environment. By statistically analyzing the factors affecting droplet deposition and establishing a multivariable relationship model, optimal droplet deposition suitable for field operation decision-making of plant protection UAVs can be quantified and evaluated. This research presents an effective technical pathway for understanding the deposition patterns of droplets sprayed by plant protection UAVs and supports the formulation of relevant pesticide application standards for plant protection UAVs.

Data availability

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

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This research was funded by the National Natural Science Foundation of China (Grant No. 31971804); Independent Innovation Project of Agricultural Science and Technology in Jiangsu Province (CX(21)3091); Suzhou Agricultural Independent Innovation Project (SNG2022061); and Suzhou Agricultural Vocational and Technical College Landmark Achievement Cultivation Project (CG[2022]02).

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Weicai, Q., Panyang, C. Analysis of the research progress on the deposition and drift of spray droplets by plant protection UAVs. Sci Rep 13 , 14935 (2023). https://doi.org/10.1038/s41598-023-40556-0

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Digital Alcohol Interventions Could Be Part of the Societal Response to Harmful Consumption, but We Know Little About Their Long-Term Costs and Health Outcomes

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  • Katarina Ulfsdotter Gunnarsson * , MSc   ; 
  • Martin Henriksson * , PhD   ; 
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Alcohol consumption causes both physical and psychological harm and is a leading risk factor for noncommunicable diseases. Digital alcohol interventions have been found to support those looking for help by giving them tools for change. However, whether digital interventions can help tackle the long-term societal consequences of harmful alcohol consumption in a cost-effective manner has not been adequately evaluated. In this Viewpoint, we propose that studies of digital alcohol interventions rarely evaluate the consequences of wider dissemination of the intervention under study, and that when they do, they do not take advantage of modeling techniques that allow for appropriately studying consequences over a longer time horizon than the study period when the intervention is tested. We argue that to help decision-makers to prioritize resources for research and dissemination, it is important to model long-term costs and health outcomes. Further, this type of modeling gives important insights into the context in which interventions are studied and highlights where more research is required and where sufficient evidence is available. The viewpoint therefore invites the researcher not only to reflect on which interventions to study but also how to evaluate their long-term consequences.

Beyond the Buzz: Unraveling the Nonexistent Long-Term Outcomes

Consumption of alcohol is in many societies the norm [ 1 ]. In Sweden, for instance, alcohol is consumed regularly by 4 of 5 adults, and approximately 1 of 3 adults are classified as harmful drinkers [ 2 , 3 ]. While evidence suggests that there is no safe limit on alcohol consumption [ 4 ], harmful drinking represents a marked increased risk of negative health and social consequences [ 1 , 4 , 5 ]. In 2016, alcohol was estimated to have contributed to approximately 6.2% of disability-adjusted life years (DALYs) among women and 5.1% of DALYs among men in Sweden, and was attributed to 4.5% and 5.7% of deaths (women and men, respectively) [ 1 ]. A health economic report showed that in 2017, alcohol consumption cost Swedish society €10 billion [ 6 ]. Sweden is not unique in this situation, as many societies worldwide face significant health and economic burdens due to the high prevalence of harmful alcohol consumption.

Reducing the harmful use of alcohol is on the World Health Organization’s list of “best buys” for tackling noncommunicable diseases [ 7 ]. Recommended actions include excise taxes on alcoholic beverages and restrictions on the retail availability of alcohol. Provisioning of psychosocial interventions for persons with harmful alcohol use is also included on the list of recommended actions, and has been operationalized by, for instance, delivering face-to-face interventions in primary health care [ 8 ]. With the ubiquity of internet connectivity in high-income countries, and increasingly in low- and middle-income countries, there is interest to provide digital psychosocial interventions to those who may benefit. Digital interventions—that is, interventions that deliver supportive content through for instance websites, mobile phone apps, text messages, or email—can support those looking for help by giving them tools to increase their motivation, form intentions for change, and give them tools to help support change [ 9 , 10 ]. Digital interventions can scale to large populations and can be designed to be anonymous, which can reduce the stigma of looking for help in face-to-face settings [ 11 - 13 ]. Studies have found that digital alcohol interventions may have positive effects on behavior in a range of different populations [ 14 - 17 ], and evidence from meta-analyses confirms these findings [ 18 - 20 ]. However, whether or not they can help tackle the long-term consequences of harmful alcohol consumption, for instance by reducing the incidence of noncommunicable diseases, and whether or not they can do so in a cost-effective manner, is still uncertain. In contrast, modeling studies have demonstrated that face-to-face interventions hold promise in mitigating health consequences while being cost-effective over the long term [ 21 ]. Thus, limiting our assessment only to the short-term effects of digital interventions means that we cannot compare the societal impacts between modalities.

We propose that studies of digital alcohol interventions rarely evaluate the consequences of wider dissemination of the intervention under study, and that when they do, they do not take advantage of modeling techniques that allow for appropriately studying consequences over a longer time horizon than the study’s period when the intervention is tested. We support our viewpoint with a pragmatic review of the literature ( Multimedia Appendix 1 [ 22 - 27 ]) and make suggestions for how future studies may evaluate the impact of digital alcohol interventions at the societal level—which includes workplace and productivity, social services and nonstatutory care, and the criminal justice sector—and not only consequences related to the health care sector such as costs for treatment. We also invite researchers to reflect on their decision-making when it comes to deciding which intervention research projects should be prioritized; in particular, if formal evaluations of the long-term consequences of dissemination are considered, or even required, in the current decision-making process.

Health economic evaluations of digital alcohol interventions are scarce. When done, the consequences of interventions are evaluated over a short period, the period over which a randomized controlled trial (RCT) is run, ranging from 4 to 12 months. While the reports identified in our pragmatic review ( Multimedia Appendix 1 [ 22 - 27 ]) all concluded that the interventions were cost-effective, they based these findings on short-term follow-up data collected during the trial period—it stands to reason that while there certainly are acute consequences from alcohol consumption, the long-term consequences are substantial and cannot be captured when evaluating such short time horizons [ 28 ]. Thus, current evaluations cannot readily answer the question being asked: What are the consequences of disseminating digital alcohol interventions into society? Considering the growth in interest and resources put into studying digital alcohol interventions, it is unfortunate that the literature cannot provide evidence of the long-term consequences of this investment.

The literature can however provide examples of long-term health economic evaluations outside the scope of digital interventions. Studies of the long-term consequences of nondigital alcohol interventions have been conducted, including the development and use of the Sheffield Alcohol Policy Model [ 29 ], which evaluates public health strategies for alcohol harm reduction. In addition, a review of nondigital brief alcohol interventions found that 14 out of the 23 included studies used modeling techniques that allowed for estimating consequences over a lifetime horizon. The majority of the 14 studies discovered that they either saved costs while improving health or incurred minimal costs compared to the health benefits [ 21 ]. Thus, there exist examples in the literature for evaluating alcohol interventions, which can inspire and be partially adopted by the digital intervention research field. Other literature can be followed to model long-term health economic outcomes, which guides the construction, analysis, and implementation of health economic models [ 30 , 31 ]. In addition, if it is important to model interactions among individuals, their propensity to use support, and relationships between multiple physical and mental health conditions, then agent-based models can be used to capture these complexities and forecast the long-term effects of interventions [ 32 , 33 ].

Importance of Health Economic Research

The importance of health economic evaluations in general, and modeling studies in particular, should not be understated. They provide input to decision-makers faced with difficult prioritization tasks, that is, if resources should be invested in disseminating a specific intervention. In many jurisdictions with a publicly funded health care system, they have long been an integral part of health policy [ 34 ] with a broad acceptance that modeling methods are required [ 35 ], not least in the evaluation of cancer drugs where intermediate outcomes (eg, progression-free survival) are routinely extrapolated to outcomes such as mortality [ 36 ]. It seems prudent that public health care efforts also should provide similar input of long-term outcomes since they often fall under the jurisdiction of public health care systems, however, this is not the case today. Their role can further be extended to also guide the prioritization of research resources. In developing an intervention, one may consider if a hypothetical intervention with a potential effect size even is worth developing and evaluating in an RCT. If such a hypothetical intervention cannot be shown to be cost-effective, then there is no reason to invest resources in its development and evaluation. Researchers are routinely asked by review panels to provide sample size estimates, which are based on anticipated effect sizes. These anticipated effect sizes commonly fall into 2 categories [ 37 - 39 ]: so-called clinical significance, which presents effect sizes large enough that the anticipated sample size is achievable, or minimum relevant effect size, which ensures that even small effect estimates are identified as statistically significant. Interestingly, these anticipated effect sizes are rarely (if ever) put to the test in a health economic evaluation where their long-term costs and health implications can be estimated; thus, a rationale for a study based on a formal evaluation of the long-term consequences of dissemination of the proposed intervention is not produced. We argue that health economic evaluations before RCTs can help reduce research waste and allow both researchers and other decision-makers to prioritize resources more effectively and, indeed, even identify cost-effective research designs [ 40 ].

It should be acknowledged that conducting health economic evaluations is not without barriers. It requires a certain technical skill set by the researcher conducting the evaluation, usually involving statistical software to create models that can extrapolate over time. It also requires epidemiological expertise to decide on input variables, as well as knowledge of the costs and consequences from both the health care and societal perspectives concerning treatment and disease. These are formidable challenges; however, approaching these challenges confers additional benefits. First, it puts into context the intervention being studied, allowing researchers to carefully think about the degree to which interventions will be adopted, their anticipated short- and long-term costs and effects, and how the novel intervention relates to existing interventions. Second, it makes it very clear where evidence is lacking from both an efficacy, epidemiologic, and economic perspective. For instance, when faced with having to decide on the risk of disease given a range of contextual variables, the process of conducting a health economic evaluation highlights where epidemiological studies are required, but also where the evidence is sufficient. Thus, by conducting a health economic evaluation, research prioritizations can become clear beyond the primary objective of the study. These prioritizations can be further refined by conducting sensitivity analyses to show which input variables affect the outcome of the evaluation the most, refining the agenda for which phenomena require further study. Third, by taking our proposed route, one that has been the gold standard for a long time in clinical medicine, decision-makers will be provided with the relevant evidence when prioritizing scarce resources that can be used for the provision of interventions or for further research. Basing evaluations solely on data from 1 RCT will yield a very precise estimate of an irrelevant cost-effectiveness estimate; thus, our advice is to follow the old advice: It is better to be roughly right than precisely wrong.

To conclude, health economic evaluations of digital alcohol interventions lack modeling of long-term societal consequences, leaving a knowledge gap concerning the degree to which they may address the burgeoning societal burdens caused by alcohol consumption. We invite researchers to reflect on their decision-making process when it comes to deciding which studies should be prioritized.

Acknowledgments

This study was conducted under the auspices of the Swedish Research Council for Health, Working Life, and Welfare (2022-00193). The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Conflicts of Interest

MB owns a private company (Alexit AB) that maintains and distributes evidence-based lifestyle interventions to be used by the public and in health care settings. Alexit AB played no role in the study design, data analysis, data interpretation, or writing of this report. KUG and MH declare no competing interests.

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Abbreviations

Edited by T Leung, T de Azevedo Cardoso; submitted 24.11.22; peer-reviewed by S Bonn, W Campbell, R Davis; comments to author 04.03.23; revised version received 19.04.23; accepted 13.02.24; published 27.03.24.

©Katarina Ulfsdotter Gunnarsson, Martin Henriksson, Marcus Bendtsen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Home » Research Data – Types Methods and Examples

Research Data – Types Methods and Examples

Table of Contents

Research Data

Research Data

Research data refers to any information or evidence gathered through systematic investigation or experimentation to support or refute a hypothesis or answer a research question.

It includes both primary and secondary data, and can be in various formats such as numerical, textual, audiovisual, or visual. Research data plays a critical role in scientific inquiry and is often subject to rigorous analysis, interpretation, and dissemination to advance knowledge and inform decision-making.

Types of Research Data

There are generally four types of research data:

Quantitative Data

This type of data involves the collection and analysis of numerical data. It is often gathered through surveys, experiments, or other types of structured data collection methods. Quantitative data can be analyzed using statistical techniques to identify patterns or relationships in the data.

Qualitative Data

This type of data is non-numerical and often involves the collection and analysis of words, images, or sounds. It is often gathered through methods such as interviews, focus groups, or observation. Qualitative data can be analyzed using techniques such as content analysis, thematic analysis, or discourse analysis.

Primary Data

This type of data is collected by the researcher directly from the source. It can include data gathered through surveys, experiments, interviews, or observation. Primary data is often used to answer specific research questions or to test hypotheses.

Secondary Data

This type of data is collected by someone other than the researcher. It can include data from sources such as government reports, academic journals, or industry publications. Secondary data is often used to supplement or support primary data or to provide context for a research project.

Research Data Formates

There are several formats in which research data can be collected and stored. Some common formats include:

  • Text : This format includes any type of written data, such as interview transcripts, survey responses, or open-ended questionnaire answers.
  • Numeric : This format includes any data that can be expressed as numerical values, such as measurements or counts.
  • Audio : This format includes any recorded data in an audio form, such as interviews or focus group discussions.
  • Video : This format includes any recorded data in a video form, such as observations of behavior or experimental procedures.
  • Images : This format includes any visual data, such as photographs, drawings, or scans of documents.
  • Mixed media: This format includes any combination of the above formats, such as a survey response that includes both text and numeric data, or an observation study that includes both video and audio recordings.
  • Sensor Data: This format includes data collected from various sensors or devices, such as GPS, accelerometers, or heart rate monitors.
  • Social Media Data: This format includes data collected from social media platforms, such as tweets, posts, or comments.
  • Geographic Information System (GIS) Data: This format includes data with a spatial component, such as maps or satellite imagery.
  • Machine-Readable Data : This format includes data that can be read and processed by machines, such as data in XML or JSON format.
  • Metadata: This format includes data that describes other data, such as information about the source, format, or content of a dataset.

Data Collection Methods

Some common research data collection methods include:

  • Surveys : Surveys involve asking participants to answer a series of questions about a particular topic. Surveys can be conducted online, over the phone, or in person.
  • Interviews : Interviews involve asking participants a series of open-ended questions in order to gather detailed information about their experiences or perspectives. Interviews can be conducted in person, over the phone, or via video conferencing.
  • Focus groups: Focus groups involve bringing together a small group of participants to discuss a particular topic or issue in depth. The group is typically led by a moderator who asks questions and encourages discussion among the participants.
  • Observations : Observations involve watching and recording behaviors or events as they naturally occur. Observations can be conducted in person or through the use of video or audio recordings.
  • Experiments : Experiments involve manipulating one or more variables in order to measure the effect on an outcome of interest. Experiments can be conducted in a laboratory or in the field.
  • Case studies: Case studies involve conducting an in-depth analysis of a particular individual, group, or organization. Case studies typically involve gathering data from multiple sources, including interviews, observations, and document analysis.
  • Secondary data analysis: Secondary data analysis involves analyzing existing data that was collected for another purpose. Examples of secondary data sources include government records, academic research studies, and market research reports.

Analysis Methods

Some common research data analysis methods include:

  • Descriptive statistics: Descriptive statistics involve summarizing and describing the main features of a dataset, such as the mean, median, and standard deviation. Descriptive statistics are often used to provide an initial overview of the data.
  • Inferential statistics: Inferential statistics involve using statistical techniques to draw conclusions about a population based on a sample of data. Inferential statistics are often used to test hypotheses and determine the statistical significance of relationships between variables.
  • Content analysis : Content analysis involves analyzing the content of text, audio, or video data to identify patterns, themes, or other meaningful features. Content analysis is often used in qualitative research to analyze open-ended survey responses, interviews, or other types of text data.
  • Discourse analysis: Discourse analysis involves analyzing the language used in text, audio, or video data to understand how meaning is constructed and communicated. Discourse analysis is often used in qualitative research to analyze interviews, focus group discussions, or other types of text data.
  • Grounded theory : Grounded theory involves developing a theory or model based on an analysis of qualitative data. Grounded theory is often used in exploratory research to generate new insights and hypotheses.
  • Network analysis: Network analysis involves analyzing the relationships between entities, such as individuals or organizations, in a network. Network analysis is often used in social network analysis to understand the structure and dynamics of social networks.
  • Structural equation modeling: Structural equation modeling involves using statistical techniques to test complex models that include multiple variables and relationships. Structural equation modeling is often used in social science research to test theories about the relationships between variables.

Purpose of Research Data

Research data serves several important purposes, including:

  • Supporting scientific discoveries : Research data provides the basis for scientific discoveries and innovations. Researchers use data to test hypotheses, develop new theories, and advance scientific knowledge in their field.
  • Validating research findings: Research data provides the evidence necessary to validate research findings. By analyzing and interpreting data, researchers can determine the statistical significance of relationships between variables and draw conclusions about the research question.
  • Informing policy decisions: Research data can be used to inform policy decisions by providing evidence about the effectiveness of different policies or interventions. Policymakers can use data to make informed decisions about how to allocate resources and address social or economic challenges.
  • Promoting transparency and accountability: Research data promotes transparency and accountability by allowing other researchers to verify and replicate research findings. Data sharing also promotes transparency by allowing others to examine the methods used to collect and analyze data.
  • Supporting education and training: Research data can be used to support education and training by providing examples of research methods, data analysis techniques, and research findings. Students and researchers can use data to learn new research skills and to develop their own research projects.

Applications of Research Data

Research data has numerous applications across various fields, including social sciences, natural sciences, engineering, and health sciences. The applications of research data can be broadly classified into the following categories:

  • Academic research: Research data is widely used in academic research to test hypotheses, develop new theories, and advance scientific knowledge. Researchers use data to explore complex relationships between variables, identify patterns, and make predictions.
  • Business and industry: Research data is used in business and industry to make informed decisions about product development, marketing, and customer engagement. Data analysis techniques such as market research, customer analytics, and financial analysis are widely used to gain insights and inform strategic decision-making.
  • Healthcare: Research data is used in healthcare to improve patient outcomes, develop new treatments, and identify health risks. Researchers use data to analyze health trends, track disease outbreaks, and develop evidence-based treatment protocols.
  • Education : Research data is used in education to improve teaching and learning outcomes. Data analysis techniques such as assessments, surveys, and evaluations are used to measure student progress, evaluate program effectiveness, and inform policy decisions.
  • Government and public policy: Research data is used in government and public policy to inform decision-making and policy development. Data analysis techniques such as demographic analysis, cost-benefit analysis, and impact evaluation are widely used to evaluate policy effectiveness, identify social or economic challenges, and develop evidence-based policy solutions.
  • Environmental management: Research data is used in environmental management to monitor environmental conditions, track changes, and identify emerging threats. Data analysis techniques such as spatial analysis, remote sensing, and modeling are used to map environmental features, monitor ecosystem health, and inform policy decisions.

Advantages of Research Data

Research data has numerous advantages, including:

  • Empirical evidence: Research data provides empirical evidence that can be used to support or refute theories, test hypotheses, and inform decision-making. This evidence-based approach helps to ensure that decisions are based on objective, measurable data rather than subjective opinions or assumptions.
  • Accuracy and reliability : Research data is typically collected using rigorous scientific methods and protocols, which helps to ensure its accuracy and reliability. Data can be validated and verified using statistical methods, which further enhances its credibility.
  • Replicability: Research data can be replicated and validated by other researchers, which helps to promote transparency and accountability in research. By making data available for others to analyze and interpret, researchers can ensure that their findings are robust and reliable.
  • Insights and discoveries : Research data can provide insights into complex relationships between variables, identify patterns and trends, and reveal new discoveries. These insights can lead to the development of new theories, treatments, and interventions that can improve outcomes in various fields.
  • Informed decision-making: Research data can inform decision-making in a range of fields, including healthcare, business, education, and public policy. Data analysis techniques can be used to identify trends, evaluate the effectiveness of interventions, and inform policy decisions.
  • Efficiency and cost-effectiveness: Research data can help to improve efficiency and cost-effectiveness by identifying areas where resources can be directed most effectively. By using data to identify the most promising approaches or interventions, researchers can optimize the use of resources and improve outcomes.

Limitations of Research Data

Research data has several limitations that researchers should be aware of, including:

  • Bias and subjectivity: Research data can be influenced by biases and subjectivity, which can affect the accuracy and reliability of the data. Researchers must take steps to minimize bias and subjectivity in data collection and analysis.
  • Incomplete data : Research data can be incomplete or missing, which can affect the validity of the findings. Researchers must ensure that data is complete and representative to ensure that their findings are reliable.
  • Limited scope: Research data may be limited in scope, which can limit the generalizability of the findings. Researchers must carefully consider the scope of their research and ensure that their findings are applicable to the broader population.
  • Data quality: Research data can be affected by issues such as measurement error, data entry errors, and missing data, which can affect the quality of the data. Researchers must ensure that data is collected and analyzed using rigorous methods to minimize these issues.
  • Ethical concerns: Research data can raise ethical concerns, particularly when it involves human subjects. Researchers must ensure that their research complies with ethical standards and protects the rights and privacy of human subjects.
  • Data security: Research data must be protected to prevent unauthorized access or use. Researchers must ensure that data is stored and transmitted securely to protect the confidentiality and integrity of the data.

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Landmark IBM error correction paper published on the cover of Nature

IBM has created a quantum error-correcting code about 10 times more efficient than prior methods — a milestone in quantum computing research.

Landmark IBM error correction paper published on the cover of Nature

Today, the paper detailing those results was published as the cover story of the scientific journal Nature. 1

Last year, we demonstrated that quantum computers had entered the Read how a paper from IBM and UC Berkeley shows a path toward useful quantum computing era of utility , where they are now capable of running quantum circuits better than classical computers can. Over the next few years, we expect to find speedups over classical computing and extract business value from these systems. But there are also algorithms with mathematically proven speedups over leading classical methods that require tuning quantum circuits with hundreds of millions, to billions, of gates. Expanding our quantum computing toolkit to include those algorithms requires us to find a way to compute that corrects the errors inherent to quantum systems — what we call quantum error correction.

Quantum error correction requires that we encode quantum information into more qubits than we would otherwise need. However, achieving quantum error correction in a scalable and fault-tolerant way has, to this point, been out of reach without considering scales of one million or more physical qubits. Our new result published today greatly reduces that overhead, and shows that error correction is within reach.

While quantum error correction theory dates back three decades, theoretical error correction techniques capable of running valuable quantum circuits on real hardware have been too impractical to deploy on quantum system. In our new paper, we introduce a new code, which we call the gross code , that overcomes that limitation.

This code is part of our broader strategy to bring useful quantum computing to the world.

While error correction is not a solved problem, this new code makes clear the path toward running quantum circuits with a billion gates or more on our superconducting transmon qubit hardware.

What is error correction?

Quantum information is fragile and susceptible to noise — environmental noise, noise from the control electronics, hardware imperfections, state preparation and measurement errors, and more. In order to run quantum circuits with millions to billions of gates, quantum error correction will be required.

Error correction works by building redundancy into quantum circuits. Many qubits work together to protect a piece of quantum information that a single qubit might lose to errors and noise.

On classical computers, the concept of redundancy is pretty straightforward. Classical error correction involves storing the same piece of information across multiple bits. Instead of storing a 1 as a 1 or a 0 as a 0, the computer might record 11111 or 00000. That way, if an error flips a minority of bits, the computer can treat 11001 as 1, or 10001 as 0. It’s fairly easy to build in more redundancy as needed to introduce finer error correction.

Things are more complicated on quantum computers. Quantum information cannot be copied and pasted like classical information, and the information stored in quantum bits is more complicated than classical data. And of course, qubits can decohere quickly, forgetting their stored information.

Research has shown that quantum fault tolerance is possible, and there are many error correcting schemes on the books. The most popular one is called the “surface code,” where qubits are arranged on a two-dimensional lattice and units of information are encoded into sub-units of the lattice.

But these schemes have problems.

First, they only work if the hardware’s error rates are better than some threshold determined by the specific scheme and the properties of the noise itself — and beating those thresholds can be a challenge.

Second, many of those schemes scale inefficiently — as you build larger quantum computers, the number of extra qubits needed for error correction far outpaces the number of qubits the code can store.

At practical code sizes where many errors can be corrected, the surface code uses hundreds of physical qubits per encoded qubit worth of quantum information, or more. So, while the surface code is useful for benchmarking and learning about error correction, it’s probably not the end of the story for fault-tolerant quantum computers.

Exploring “good” codes

The field of error correction buzzed with excitement in 2022 when Pavel Panteleev and Gleb Kalachev at Moscow State University published a landmark paper proving that there exist asymptotically good codes — codes where the number of extra qubits needed levels off as the quality of the code increases.

This has spurred a lot of new work in error correction, especially in the same family of codes that the surface code hails from, called quantum low-density parity check, or qLDPC codes. These qLDPC codes are quantum error correcting codes where the operations responsible for checking whether or not an error has occurred only have to act on a few qubits, and each qubit only has to participate in a few checks.

But this work was highly theoretical, focused on proving the possibility of this kind of error correction. It didn’t take into account the real constraints of building quantum computers. Most importantly, some qLDPC codes would require many qubits in a system to be physically linked to high numbers of other qubits. In practice, that would require quantum processors folded in on themselves in psychedelic hyper-dimensional origami, or entombed in wildly complex rats’ nests of wires.

In our paper, we looked for fault-tolerant quantum memory with a low qubit overhead, high error threshold, and a large code distance.

High-threshold and low-overhead fault-tolerant quantum memory

In our Nature paper, we specifically looked for fault-tolerant quantum memory with a low qubit overhead, high error threshold, and a large code distance.

Let’s break that down:

Fault-tolerant: The circuits used to detect errors won't spread those errors around too badly in the process, and they can be corrected faster than they occur

Quantum memory: In this paper, we are only encoding and storing quantum information. We are not yet doing calculations on the encoded quantum information.

High error threshold: The higher the threshold, the higher amount of hardware errors the code will allow while still being fault tolerant. We were looking for a code that allowed us to operate the memory reliably at physical error rates as high as 0.001, so we wanted a threshold close to 1 percent.

Large code distance: Distance is the measure of how robust the code is — how many errors it takes to completely flip the value from 0 to 1 and vice versa. In the case of 00000 and 11111, the distance is 5. We wanted one with a large code distance that corrects more than just a couple errors. Large-distance codes can suppress noise by orders of magnitude even if the hardware quality is only marginally better than the code threshold. In contrast, codes with a small distance become useful only if the hardware quality is significantly better than the code threshold.

Low qubit overhead: Overhead is the number of extra qubits required for correcting errors. We want the number of qubits required to do error correction to be far less than we need for a surface code of the same quality, or distance.

We’re excited to report that our team’s mathematical analysis found concrete examples of qLDPC codes that met all of these required conditions. These fall into a family of codes called “Bivariate Bicycle (BB)” codes. And they are going to shape not only our research going forward, but how we architect physical quantum systems.

The gross code

While many qLDPC code families show great promise for advancing error correction theory, most aren’t necessarily pragmatic for real-world application. Our new codes lend themselves better to practical implementation because each qubit needs only to connect to six others, and the connections can be routed on just two layers.

To get an idea of how the qubits are connected, imagine they are put onto a square grid, like a piece of graph paper. Curl up this piece of graph paper so that it forms tube, and connect the ends of the tube to make donut. On this donut, each qubit is connected to its four neighbors and two qubits that are farther away on the surface of the donut. No more connections needed.

The good news is we don’t actually have to embed our qubits onto a donut to make these codes work — we can accomplish this by folding the surface differently and adding a few other long-range connectors to satisfy mathematical requirements of the code. It’s an engineering challenge, but much more feasible than a hyper-dimensional shape.

We explored some codes that have this architecture and focused on a particular [[144,12,12]] code. We call this code the gross code because 144 is a gross (or a dozen dozen). It requires 144 qubits to store data — but in our specific implementation, it also uses another 144 qubits to check for errors, so this instance of the code uses 288 qubits. It stores 12 logical qubits well enough that fewer than 12 errors can be detected. Thus: [[144,12,12]].

Using the gross code, you can protect 12 logical qubits for roughly a million cycles of error checks using 288 qubits. Doing roughly the same task with the surface code would require nearly 3,000 qubits.

This is a milestone. We are still looking for qLDPC codes with even more efficient architectures, and our research on performing error-corrected calculations using these codes is ongoing. But with this publication, the future of error correction looks bright.

fig1-Tanner Graphs of Surface and Bivariate Bicycle Codes.png

Fig. 1 | Tanner graphs of surface and BB codes. a, Tanner graph of a surface code, for comparison. b, Tanner graph of a BB code with parameters [[144, 12, 12]] embedded into a torus. Any edge of the Tanner graph connects a data and a check vertex. Data qubits associated with the registers q(L) and q(R) are shown by blue and orange circles. Each vertex has six incident edges including four short-range edges (pointing north, south, east and west) and two long-range edges. We only show a few long-range edges to avoid clutter. Dashed and solid edges indicate two planar subgraphs spanning the Tanner graph, see the Methods. c, Sketch of a Tanner graph extension for measuring Z ˉ \={Z} Z ˉ and X ˉ \={X} X ˉ following ref. 50, attaching to a surface code. The ancilla corresponding to the X ˉ \={X} X ˉ measurement can be connected to a surface code, enabling load-store operations for all logical qubits by means of quantum teleportation and some logical unitaries. This extended Tanner graph also has an implementation in a thickness-2 architecture through the A and B edges (Methods).

Syndrome measurement circuit

Fig. 2 | Syndrome measurement circuit. Full cycle of syndrome measurements relying on seven layers of CNOTs. We provide a local view of the circuit that only includes one data qubit from each register q(L) and q(R) . The circuit is symmetric under horizontal and vertical shifts of the Tanner graph. Each data qubit is coupled by CNOTs with three X-check and three Z-check qubits: see the Methods for more details.

Why error correction matters

Today, our users benefit from novel error mitigation techniques — methods for reducing or eliminating the effect of noise when calculating observables, alongside our work suppressing errors at the hardware level. This work brought us into the era of quantum utility. IBM researchers and partners all over the world are exploring practical applications of quantum computing today with existing quantum systems. Error mitigation lets users begin looking for quantum advantage on real quantum hardware.

But error mitigation comes with its own overhead, requiring running the same executions repeatedly so that classical computers can use statistical methods to extract an accurate result. This limits the scale of the programs you can run, and increasing that scale requires tools beyond error mitigation — like error correction.

Last year, we debuted a new roadmap laying out our plan to continuously improve quantum computers over the next decade. This new paper is an important example of how we plan to continuously increasing the complexity (number of gates) of the quantum circuits that can be run on our hardware. It will allow us to transition from running circuits with 15,000 gates to 100 million, or even 1 billion gates.

  • Rafi Letzter
  • Impact Science
  • Quantum Error Correction & Mitigation
  • Note 1 :  Read how a paper from IBM and UC Berkeley shows a path toward useful quantum computing   ↩︎

Bravyi, S., Cross, A.W., Gambetta, J.M. et al. High-threshold and low-overhead fault-tolerant quantum memory. Nature 627, 778–782 (2024). https://doi.org/10.1038/s41586-024-07107-7 ↩

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  1. Writing a Review Paper: What,Why, How?

  2. Kinds and Classification of Research

  3. Lecture 01: Basics of Research

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  1. Types of Research

    This type of research is subdivided into two types: Technological applied research: looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes. Scientific applied research: has predictive purposes. Through this type of research design, we can ...

  2. Types of Research Designs Compared

    Types of Research Designs Compared | Guide & Examples. Published on June 20, 2019 by Shona McCombes.Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do.. There are many ways to categorize different types of research.

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  4. Research Paper

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  5. What Is a Research Design

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    Experimental research paper. This type of research paper basically describes a particular experiment in detail. It is common in fields like: biology. chemistry. physics. Experiments are aimed to explain a certain outcome or phenomenon with certain actions. You need to describe your experiment with supporting data and then analyze it sufficiently.

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    Explanatory. Analytical research. Types of research according to the mode of inquiry. Quantitative research. Quantitative research. Types of research according to the aims of the research approach. Longitudinal research. Cross-sectional research. Conceptual research.

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    Types of Research are as follows: Applied Research: This type of research aims to solve practical problems or answer specific questions, often in a real-world context. Basic Research: This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.

  9. What Is Research Design? 8 Types + Examples

    Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data. Research designs for quantitative studies include descriptive, correlational, experimental and quasi-experimenta l designs. Research designs for qualitative studies include phenomenological ...

  10. Research Design

    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.

  11. Research Guides: Organizing Your Social Sciences Research Paper: Types

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    The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

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    Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...

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    Most research assignments ask you to engage in one of two approaches: Explore and evaluate (present an analysis) Persuade (present an argument) The tabs below will give you more information about each type. Your professor may allow you to choose between these strategies or may ask you to use only one. If you're not sure which type you should ...

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    Some more types of research papers. In addition to the above-detailed types of research papers, there are many more types, including review papers, case study papers, comparative research papers and so on. Review papers provide a detailed overview and analysis of existing research on a particular topic. The key objective of a review paper is to ...

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    Although research paper assignments may vary widely, there are essentially two basic types of research papers. These are argumentative and analytical.. Argumentative. In an argumentative research paper, a student both states the topic they will be exploring and immediately establishes the position they will argue regarding that topic in a thesis statement.

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    It is perfect for papers in Psychology, Journalism, Healthcare, and subjects where accuracy is vital. Secondary. This research type of work is mainly developed with sources that represent secondary references. These include books in print or found online, scientific journals, peer-reviewed documents, etc.

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    Argumentative research papers are prevalent in disciplines such as philosophy, social sciences, and humanities, where different perspectives and debates are common. 6. Case Study Research Papers: Case study research papers provide an in-depth analysis of a particular individual, group, organization, or event.

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