research design classification ppt

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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Research Design Classification

Aug 04, 2014

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NTR 629 - Week 2. Research Design Classification. How Study Designs Differ. Number of observations made Directionality of exposure Data collection methods Timing of data collection Unit of observation Availability of subjects. Study Design Approaches. Experimental Approaches.

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• approaches observational approaches
• clinical trials
• most rigorous
• nonequivalent control group design
• less rigorous
• experimental

Presentation Transcript

NTR 629 - Week 2 Research DesignClassification

How Study Designs Differ • Number of observations made • Directionality of exposure • Data collection methods • Timing of data collection • Unit of observation • Availability of subjects

Study Design Approaches Experimental Approaches Observational Approaches No manipulation No randomization of study subjects/units Less rigorous than experimental designs Analytic or descriptive Analytic studies E.g., many ecologic studies, case-control studies, cohort studies Descriptive studies: E.g. cross-sectional surveys • Manipulation (exposure of interest controlled by the investigator) • Hypothesis testing • Examines cause-effect • Quantitative and analytic • Experimental Design • Most rigorous design. Randomization of study subjects/units • Quasi-experimental Design • Less rigorous, because no randomization of study subjects/units

Classification Based on Purpose Analytical Descriptive Case Study Case Series Developmental Correlational Descriptive Survey Field/Ethnographic • Experimental • Quasi Experimental • Pre-Experimental • Cohort studies • Case Control (or Single Subject) • Historiography • Analytical Survey • Content Analysis • Causal-Comparative

Design Characteristics Analytical Descriptive No true hypothesis Establishes a relationship Describes state of nature at point in time No control of variables Recording of observations Primarily to totally qualitative • Test hypothesis • Allows detection of causal associations • Numerical data – quantitative.

Analytical Designs (Part 1):Experimental Designs

True Experimental Design • Pretest post-test control group design • TWO (or more) groups: • Random/control group O1 O2 • Random/experimental group O1 X O2 • Caution with within-session variation between treatments A and B… control conditions. Pretest important if need to check equivalence of groups. • Key for Study Design Symbols: • O1 = observation 1 (measurement of dependent variable) • X = manipulated variable; independent variable • O2 = observation 2 (measurement of same dependent variable as O1)

True Experimental Design • Pretest post-test control group design • THREE groups: • Random/control group O1 O2 • Random/experimental group A O1 XA O2 • Random/experimental group B O1 XB O2 • Caution with within-session variation between treatments A and B… control conditions. Pretest important if need to check equivalence of groups.

True Experimental Design • Post-test only control group design • TWO (or more) groups: • Random/control group O2 • Random/experimental group X O2 • No pretest? Assume equivalence with randomization. No interaction effect with pretesting.

True Experimental Design • Solomon four group design • FOUR groups: • Random/experimental group O1 X O2 • Random/control group 1 O1 O2 • Random/control group 2 X O2 • Random/control group 3 O2 • Important if taking pretest influences post-test.

Quasi Experimental Design • Nonequivalent control group design • TWO groups: • Experimental group O1 X O2 • Control group O1 O2 • Uses intact groups (e.g., class); no randomization

Quasi Experimental Design • Static group design • TWO groups: • Experimental group X O2 • Control group O2 • Uses intact groups (e.g., class); no randomization

Quasi Experimental Design • Counterbalanced design • FOUR (or more) groups (A, B, C, D) and FOUR (or more) treatment variations (1, 2, 3, 4), with exposure at different times during study: • Replication Treatment Variations XA XB XC XD 1 A B C D 2 B D A C 3 C A D B 4 D C B A • Uses intact groups (e.g., class); no randomization

Quasi Experimental Design • Single subject design • ONE subject: • Experimental subject base-O1 X withdraw-X O2 • “Behavioral”, natural setting, little generalizability

Quasi Experimental Design • One group time series design • ONE group: • Experimental group O1 O2 O3 O4 X O5 O6 O7 O8 • Determine if effect of X, and if X is short-term effect.

Quasi Experimental Design • Control group time series design • TWO groups: • Experimental group O1 O2 O3 O4 X O5 O6 O7 O8 • Control group O1 O2 O3 O4 O5 O6 O7 O8 • Helps control selection-maturation effects.

Quasi Experimental Design • Control group time series design • FIVE (or more) groups: • Experimental group A O1 X O2 • Experimental group B O1 X O2 • Experimental group C O1 X O2 • Experimental group D O1 X O2 • Experimental group E O1 X O2 • Helps control maturation, pretest, regression, history

Factorial Design • 2x2 factorial design • To examine interaction effects of two or more independent variables (X) and test several H0 simultaneously. Teaching method (X1)Length of period (X2) 50 minutes 30 minutes • Discussion O1 O2 • Lecture O3 O4

Factorial Design • There are many variations of factorial designs. The variables can have multiple levels. E.g.,: • 2x3 design • two X (X = manipulation): one with two levels, one with three levels • 3x3 design • Three X, each with three levels • 2x2x2 design • Three independent variables, each varied two ways

Controlled Clinical Trials Advantages Limitations Artificial setting Limited scope of potential impact Adherence to protocol is difficult to enforce Possible ethical dilemmas • Experimental Design • Comparing outcomes in treated group compared to an equivalent control group • Participants in both groups are enrolled (random assignment into group), treated, and followed over the same time period • Single or double-blinded. • Used to test efficacy of preventive (prophylactic) or therapeutic (curative) measures • Multicenter trials--results from several researchers pooled.

Schematic Diagram of a Clinical Trial SAMPLE Nonparticipants Randomization to groups Intervention group Control group Lost to follow-up Measure outcome Measure outcome

Clinical Trial Crossover Designs • Any change of treatment for a patient in a clinical trial involving a switch of study treatments. • Planned crossovers • Protocol is developed in advance, and the patient may serve as his or her own control. • Unplanned crossovers • Exist for various reasons, such as patient’s request to change treatment. • Members of both groups receive both treatment regimens • Group 1 receives treatment A then treatment B • Group 2 receives treatment B then treatment A Treatment A Treatment B

Community Trials Advantages Limitations Inferior to clinical trials with respect to ability to control entrance into study, delivery of the intervention, and monitoring of outcomes. Fewer study units are capable of being randomized, which affects comparability. Affected by population dynamics, secular trends, and nonintervention influences • Represents the only way to estimate directly the impact of change in behavior or modifiable exposure on the incidence of disease. • Community intervention trials determine the potential benefit of new policies and programs. • Community refers to a defined unit, e.g., a county, state, or school district.

Community Trials - Steps • Community trials start by: • Determining eligible communities and their willingness to participate • Collect baseline measures of the problem to be addressed in the communities, e.g., disease rates, knowledge, attitudes, and practices • Communities are randomized (intervention and control) • Followed over time • Outcomes of interest are measured

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Types of research design - PowerPoint PPT Presentation

Types of research design

Types of research design experiments chapter 8 in babbie & mouton (2001) introduction to all research designs all research designs have specific objectives they ... – powerpoint ppt presentation.

• Chapter 8 in Babbie Mouton (2001)
• Introduction to all research designs
• All research designs have specific objectives they strive for
• Have different strengths and limitations
• Have validity considerations
• When we say that a knowledge claim (or proposition) is valid, we make a JUDGEMENT about the extent to which relevant evidence supports that claim to be true
• Is the interpretation of the evidence given the only possible one, or are there other plausible ones?
• "Plausible rival hypotheses" potential alternative explanations/claims
• e.g. New York City's "zero tolerance" crime fighting strategy in the 1980s and 1990s - the reverse of the "broken windows" effect
• Explanatory rather than descriptive
• Different from correlational research - one variable is manipulated (IV) and the effect of that manipulation observed on a second variable (DV)
• "Animals respond aggressively to crowding" (causal)
• "People with premarital sexual experience have more stable marriages" (noncausal)
• Independent and dependent variables
• Pre-testing and post-testing
• Experimental and control groups
• Dependent (DV)
• Independent (IV)
• To off-set the effects of the experiment itself to detect effects of the experiment itself
• The IV is an active variable it is manipulated
• The participants who receive one level of the IV are equivalent in all ways to those who receive other levels of the IV
• 1. Selecting subjects to participate in the research
• Careful sampling to ensure that results can be generalized from sample to population
• The relationship found might only exist in the sample need to ensure that it exists in the population
• Probability sampling techniques
• 2. How the sample is divided into two or more groups is important
• to make the groups similar when they start off
• randomization - equal chance
• matching - similar to quota sampling procedures
• match the groups in terms of the most relevant variables e.g. age, sex, and race
• One-shot case study
• No real comparison
• Milgram's study on obedience
• Obedience to authority
• The willingness of subjects to follow E's orders to give painful electrical shocks to another subject
• A real, important issue here how could "ordinary" citizens, like many Germans during the Nazi period, do these incredibly cruel and brutal things?
• If a person is under allegiance to a legitimate authority, under what conditions will the person defy the authority if s/he is asked to carry out actions clearly incompatible with basic moral standards?
• We want to find out whether a family literacy programme enhances the cognitive development of preschool-age children.
• Find 20 families with a 4-year old child, enrol the family in a high-quality family literacy programme
• Administer a pretest to the 20 children - they score a mean of say 50 on the cognitive test
• The family participates in the programme for twelve months
• Administer a post-test to the 20 children now they score 75 on the test - a gain of 25
• 1 The children gained 25 points on average in terms of their cognitive performance
• 2 the family literacy programme caused the gain in scores
• VALIDITY - rival explanations
• We know the structure of research
• We understand designs
• We know the requirements of "good" research
• Then we can evaluate a study
• Is it good? Can we believe its conclusions?
• Back to plausible rival hypotheses
• If the design is not valid, then the conclusions drawn are not supported it is like not doing research at all
• Validity of designs come in two parts
• Internal validity
• can the design sustain the conclusions?
• External validity
• can the conclusions be generalized to the population?
• Each design is only capable of supporting certain types of conclusions
• e.g. only experiments can support conclusions about causality
• Says nothing about if the results can be applied to the real world (generalization)
• Generally, the more controlled the situation, the higher the internal validity
• The conclusions drawn from experimental results may not accurately reflect hat has gone on in the experiment itself
• These sources often discussed as part of experiments, but can be applied to all designs (e.g. see reactivity)
• Historical events may occur that will be confounded with the IV
• Especially in field research (compare the control in a laboratory, e.g. nonsense syllables in memory studies
• Changes over time can be caused by a natural learning process
• People naturally grow older, tired, bored, over time
• People realize they are being studied, and respond the way they think is appropriate
• The very act of studying something may change it
• In qualitative research, the "on stage" effects
• Improved performance because of the researcher's presence - people became aware that they were in an experiment, or that they were given special treatment
• Especially for people who lack social contacts, e.g. residents of nursing homes, chronic mental patients
• When a person expects a treatment or experience to change her/him, the person changes, even when the "treatment" is know to be inert or ineffective
• Medical research
• "The bedside manner", or the power of suggestion
• Pygmalion effect - self-fulfilling prophecies of e.g. teachers' expectancies about student achievement
• Experimenters may prejudge their results - experimenter bias
• Double blind experiments
• Both the researcher and the research participant are "blind" to the purpose of the study.
• They don't know what treatment the participant is getting
• Instruments with low reliability lead to inaccurate findings/missing phenomena
• e.g. human observers become more skilled over time (from pretest to posttest) and so report more accurate scores at later time points
• Studying extreme scores can lead to inflated differences, which would not occur in moderate scorers
• Selection subjects for the study, and assigning them to E-group and C-group
• Look out for studies using volunteers
• Sometimes called experimental (or subject) mortality
• If subjects drop out, it creates a bias to those who did not
• e.g. comparing the effectiveness of family therapy with discussion groups for treatment of drug addiction
• addicts with the worst prognosis more likely to drop out of the discussion group
• will make it look like family therapy does less well than discussion groups, because the "worst cases" were still in the family therapy group
• When subject can communicate to each other, pass on some information about the treatment (IV)
• In real life, people may feel sorry for C-group who does not get "the treatment" - try to give them something extra
• e.g. compare usual day care for street children with an enhanced day treatment condition
• service providers may very well complain about inequity, and provide some enhanced service to the children receiving usual care
• C-group may "work harder" to compete better with the E-group
• Opposite to compensatory rivalry
• May feel deprived, and give up
• e.g. giving unemployed high school dropouts a second chance at completing matric via a special education programme
• if we assign some of them to a control group, who receive "no treatment", they may very well become profoundly demoralized
• Can the findings of the study be generalized?
• Do they speak only of our sample, or of a wider group?
• To what populations, settings, treatment variables (IV's), and measurement variables can the finding be generalized?
• Mainly questions about three aspects
• Research participants
• Independent variables, or manipulations
• Dependent variables, or outcomes
• Says nothing about the truth of the result that we are generalizing
• External validity only has meaning once the internal validity of a study has been established
• Internal validity is the basic minimum without which an experiment is uninterpretable
• Our interest in answering research questions is rarely restricted to the specific situation studied - our interest is in the variables, not the specific details of a piece of research
• But studies differ in many ways, even if they study the same variables
• operational definitions of the variables
• subject population studied
• procedural details
• Generally bigger samples with valid measures lead to better external validity
• Subject selection - Selecting a sample which does not represent the population well, will prevent generalization
• Interaction between the testing situation and the experimental stimulus
• When people have been sensitized to the issues by the pre-test
• Respond differently to the questionnaires the second time (post-test)
• Operationalization
• We take a variable with wide scope and operationalize it in a narrow fashion
• Will we find the same results with a different operationalization of the same variable?
• "natural" - e.g. disaster research
• Static-group comparison type
• Non-equivalent experimental and control groups
• Manipulating the IV
• Sorting out extraneous variables
• Articifiality - a generalization problem
• Limited range of questions
• Donald Campbell often cited Neurath's metaphor
• "in science we are like sailors who must repair a rotting ship while it is afloat at sea. We depend on the relative soundness of all other planks while we replace a particularly weak one. Each of the planks we now depend on we will in turn have to replace. No one of them is a foundation, nor point of certainty, no one of them is incorrigible"

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Types of research designs

Related Papers

Rachel Irish Kozicki

Evidence-Based Nursing

Jenny Ploeg

Lova Rakotoarison

Velia Nuñez

mark vince agacite

Naeem Tabassum

The objective of this chapter is to present the research design and statistical approach applied in this work. We explain the research philosophy used and justify the research approach.

Gerardo Munck

Tutors India

High-quality research designs must be selected by the researchers so that, the researchers can use those designs to convey those ideas readers in an understandable mode. Both of these tasks are difficult for researchers. The significant features of the research design are framed in an evidently complete form which can be used in qualitative, quantitative, and combination of methods for research. Specific features like inquiry (I), a model of the world (M), a data strategy (D), and a strategy for an answer (A) must be defined in the design approach. These features which provide adequate information needed for researchers and readers must be introduced in the code so as to understand the techniques to analyze bias, accuracy, and power of qualitative inferences. The selection of articles was based on the criterion of inclusion-exclusion that is approved by all authors. The dataset consists of (1) Articles and reviews (2) Studies related to the medical sector (3) Studies in the English language (4) Studies related to big data analytics. Click the link to read: https://bit.ly/2ZWZPM1 Contact: Website: www.tutorsindia.com Email: [email protected] United Kingdom: +44-1143520021 India: +91-4448137070 Whatsapp Number: +91-8754446690

Negus S Rudison-Imhotep, Ph.D., MPA

Compare and contrast the characteristics of external, internal, and construct validity.

ResearchGate

Joyzy P Egunjobi

Research Method Vs Research Design Students are usually confused about research methods and research designs. These may appear the same, but they are different. Research Methods Research methods can be conceived as various processes, procedures, and tools employed to collect and analyze research data. They are approaches used to execute research plans. A research method is a research paradigm or philosophical framework that research is based. There are three commonest methods in research namely, quantitative, qualitative, and mixed methods. These methods are an umbrella for various research designs. Research Designs Research designs are the overall research structure of a study which help to ensure that the data collected effectively answers the research question(s). Research designs can be Descriptive (e.g., case-study, naturalistic observation, survey), Correlational (e.g., case-control study, observational study), Experimental (e.g., field experiment, controlled experiment, quasiexperiment), Review (literature review, systematic review), and Meta-analytic (meta-analysis) in nature. They can, however, be grouped under research methods. Note that the nature of the research will determine the research method as well as the appropriate research design.

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Clinical research study designs: The essentials

Ambika g. chidambaram.

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

Maureen Josephson

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

Introduction

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

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

Overview of clinical research study designs

Observational study designs

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

Classification of observational study designs

Case reports and case series

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

Ecologic study

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

Cross‐sectional study

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

Case‐control study

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

Case‐control study design

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

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

Cohort study

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

Cohort study design

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

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

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

Case‐control studies based within a defined cohort

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

Nested case‐control study

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

Case‐cohort Study

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

Experimental study design

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

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

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

Experimental study designs

Clinical trial

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

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

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

Randomized clinical trial

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

Non‐randomized clinical trial

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

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

Cross‐over clinical trial

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

Factorial trial

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

Community trial

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

Field trial

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

Overview of methodologies used within a study design

Randomization.

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

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

Simple randomization

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

Block randomization

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

Stratified randomization

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

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

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

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

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

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

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

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

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

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

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

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

Overview of drug development in the United States of America

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

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

CONFLICT OF INTEREST

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

30+ Best Research Presentation Templates for PowerPoint (PPT)

Finding the right PowerPoint template plays an important part in getting your message across to the audience during a presentation. And it’s especially true for research presentations.

Using the right colors, graphs, infographics, and illustrations in your slides is the key to delivering information more effectively and making your presentation a success.

Today, we handpicked a great collection of research presentation PowerPoint templates for you to make the perfect slideshows for various types of research papers and studies.

Whether you’re preparing for a presentation at a school, event, or conference, there are templates in this list for all purposes. Let’s dive in.

2 Million+ PowerPoint Templates, Themes, Graphics + More

Download thousands of PowerPoint templates, and many other design elements, with a monthly Envato Elements membership. It starts at $16 per month, and gives you unlimited access to a growing library of over 2,000,000 presentation templates, fonts, photos, graphics, and more.

Pitch Deck Templates

Startup pitch deck.

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Mystify Presentation

Minimal PPT Templates

Clean & clear.

Ciri Template

Animated PPT Templates

Fully animated.

Explore PowerPoint Templates

Science & Research Presentation PowerPoint Template

This PowerPoint template is a perfect choice for preparing a research presentation to share your scientific findings and reports.

The template has 30 unique slides with unlimited color options. There are a few infographics included in the slideshow as well.

Why This Is A Top Pick

The presentation has a very modern and creative design where you can showcase your data and information in an attractive way. You won’t be making boring research presentations ever again.

Labvire – Research Presentation PowerPoint Template

Labvire is another modern PowerPoint template you can use for various types of research presentations. It’s also ideal for laboratory-related research presentations. The template has fully customizable slide layouts with editable charts, graphs, and more. You can choose from more than 40 unique slide designs as well.

Novalabs – Science Research PowerPoint Template

Novalabs PowerPoint template features a highly visual and attractive design. The template includes 36 different slides that feature large image placeholders for adding a more visual look to your presentations. There are lots of editable graphics, shapes, and tables included in the template too. Feel free to customize them however you like.

Research & Development PowerPoint Template

The minimal and clean design of this PowerPoint template makes it a great choice for delivering more effective research presentations. With fewer distractions in each slide, you’ll be able to convey your message more easily. The template comes with 30 unique slides. You can change the colors, fonts, and shapes to your preference as well.

Marketing Research Presentation PowerPoint Template

When talking about research presentations, we can’t forget about marketing research. Most sales and marketing meetings usually include a sophisticated marketing research presentation. This PowerPoint template will help you design those research presentations without effort. It includes a total of 150 slides, featuring 30 unique slides in 5 different color schemes.

Free Business Market Research Presentation Template

This is a free PowerPoint template designed for making business market research presentations. It gives you 27 different and fully customizable slides to create professional slideshows for your business meetings.

Free Business Data Analysis & Research Presentation

With this PowerPoint template, you can create colorful and creative business research and data analysis presentation without any design skills. It includes 35 unique slides with lots of infographics and editable shapes. The template is free to use as well.

Lernen – Research Thesis PowerPoint Presentation

Larnen is the ideal PowerPoint template for making research slideshows for your thesis presentations. It includes 30 unique slides that are available in light and dark color themes. It also has editable charts and graphs.

Aristo – Research Academic PowerPoint Presentation

This PowerPoint template is also made with academic research presentations in mind. The template has a professional design with clean layouts and light colors. It comes with more than 30 different slides.

Biosearch – Science Research PowerPoint Template

You can use this PowerPoint template to make professional presentations to present research data and results. It lets you choose from 40 different slides and 90 color themes. The slides are available in both light and dark color themes as well.

Neolabs – Laboratory & Science Research PPT

Neolabs is another science research presentation made with laboratory research teams in mind. You can use it to make effective slideshows to present your research findings. There are 30 unique slides in this template.

Free Business Cost Analysis PowerPoint Template

This is a free PowerPoint and Google Slides template that comes with 35 unique slides. It’s ideal for making research presentations related to business financials.

Research & Case Study PowerPoint Template

Create the perfect case study presentation using your research data with this PowerPoint template. It includes a modern slide design with infographics and charts for effectively presenting your data.

Liron Labs – Laboratory Research PowerPoint Template

Another PowerPoint template for laboratory research presentations. This template includes 15 useful slide layouts with editable graphics, free fonts, and image placeholders. You can edit and customize the colors and text as well.

Research Thesis PowerPoint Template

Make an attractive and creative research thesis presentation using this PowerPoint template. There are over 30 unique slides in this template. You can either use dark or light color themes to create your presentations.

Colorful Thesis Research PowerPoint Template

If you want to make your research presentations look more colorful and creative, this PowerPoint template is for you. It has 15 different slides with fully customizable layouts. It has editable shapes, free fonts, and image placeholders too.

Free Data Analysis Research PowerPoint Template

This PowerPoint template is also free to download. You can also customize it using PowerPoint or Google Slides. This template is ideal for marketing agencies and teams for presenting research and data analysis.

Laboratory & Science Research PowerPoint Template

You can make more convincing and unique lab research presentations using this PowerPoint template. It features a creative design that will easily attract the attention of your audience. You can use it to make various other science and research presentations too. The template includes 30 unique slides.

The Biologist – Research Presentation PowerPoint Template

Just as the name suggests, this PowerPoint template is designed with biology and science-related presentations in mind. It includes many useful slide layouts that can be used to make various types of research presentations. There are 30 different slide designs included in this template with editable shapes and colors.

Modern Science & Research PowerPoint Template

If you’re looking for a PowerPoint template to create a modern-looking research presentation, this template is perfect for you. It features a collection of modern and attractive slides with lots of space for including images, icons, and graphs. There are 30 unique slides in the template with light and dark color themes to choose from.

Marketing Report & Research PowerPoint Template

This PowerPoint template doubles as both a research and report slideshow. You can use it to create various marketing reports as well as marketing research presentations. It comes with 30 slides that feature minimal and clean designs. It includes lots of editable charts, infographics, and tables as well.

Market Research Presentation PowerPoint Template

Another modern PowerPoint template for making market research presentations. This template includes 25 unique slides with master slides, image placeholders, and editable colors. The template is ideal for marketing agencies and corporate businesses.

Free Academic Research Thesis PowerPoint Template

This free PowerPoint template is designed for defending your academic research thesis dissertation. Needless to say, it’s a useful template for academics as well as teachers. The template features 23 unique slide layouts with customizable designs.

Free Economics Research Thesis Presentation Template

You can use this free template to create thesis and research presentations related to economics. It’s useful for academic students and gives you the freedom to choose from 21 slide layouts to make your own presentations.

Labia – Research Presentation Powerpoint Template

Labia is a research presentation template made for professionals. It comes with a set of modern slides with multipurpose designs. That means you can customize them to make many different types of research presentations. There are 30 unique slides included in this template that come in 5 different color themes.

Medical Research Infographics & Powerpoint Slides

You’ll be using lots of charts, graphs, and infographics in your presentations to showcase data in visual form. Not to mention that visuals always work well for attracting the audience’s attention. You can use the infographic slides in this template to create better research presentations. Each slide features a unique infographic with animated designs.

Foreka – Biology Education & Research Presentation PPT

Foreka is a PowerPoint template made for educational presentations, especially for covering topics related to biology. But it can also be customized to present your research presentations. The slides have very useful layouts that are most suitable for making research slide designs. There are 30 slides included with light and dark color themes.

Maua – Aesthetic Business Research PowerPoint Template

This PowerPoint template is suitable for making elegant and stylish business reports and business research presentations. It’s especially great for making background research and competitor research slideshows. The template comes with 30 slides featuring master slides, image placeholders, and more.

World Data Scientist Powerpoint Presentation Template

You can use this PowerPoint template to create research presentations for many different types of topics, industries, and projects. The template includes lots of data-centric slides where you can easily showcase your data in visual form. There are 30 unique slides included with the template as well.

Free SWOT Analysis Infographics PowerPoint Template

SWOT analysis is a commonly used methodology in business research presentations. With this free PowerPoint template, you can create stylish SWOT analysis infographics for your presentations. It includes SWOT infographics in 30 different styles.

Free Market Research Presentation Infographics PPT

This is a collection of free PowerPoint slides that feature various styles of infographics you can use in your business and market research presentations. There are 30 different infographic slides included in this template. You can edit, change colors, and customize them however you like.

Sinara – Science & Research Powerpoint Template

Sinara is a brilliant PowerPoint template you can use to craft a professional presentation for science-related research and reports. It’s available in 3 different color schemes as well as the option to customize the colors to your preference. The template comes in light and dark themes too.

Political Science and Research PowerPoint Template

This PowerPoint template will be quite useful to political science and international relations students. It features a total of 150 slides you can use to create attractive presentations for your research and methodologies. There are slides in 5 different color schemes.

How to Make a Research Poster in PowerPoint

We bet you didn’t know that you could actually design posters in PowerPoint. Well, you can and it’s very easy to do so.

The easiest way to make a poster in PowerPoint is to use a pre-made template like the one above.

You can easily copy one of the slides from a template, and resize the slide dimensions to create a vertical poster. Then add a title with a few lines of text and you’ll have yourself a poster.

Or, if you want to craft a poster from scratch, you can read our complete guide on how to create posters in PowerPoint with step-by-step instructions.

For more useful presentation templates, be sure to check out our best educational PowerPoint templates collection.

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Our approach

• Responsibility
• Infrastructure
• Try Meta AI

RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

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