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3 main types of Design of Experiments (DOE) designs

Space fills. Fractional factorials. Full factorials. Optimal designs. Design of Experiments (DOE) offers a daunting compilation of types of design. As a beginner, understanding which one is right for your needs can feel like an impossible task.

To help you make the right choice, we’ll walk you through: 

• What to bear in mind when choosing a DOE design
• 3 main types of DOE designs that all beginners should know about, plus when and how to use them in your DOE campaign

Need a quick primer on DOE before we get started?

What is Design of Experiments (DOE)?

• Why should I use Design of Experiments in the Life Sciences?
• The DOE process: A step-by-step guide

Choosing the right DOE design: What you need to know

There are just a few basic types, but lots of variations.

The number of possible designs on offer can sometimes seem a bit overwhelming. 

Luckily, most fall into a few basic categories of design—or families, if you will.

The thing that defines these families is the DOE stage they’re most useful for. 

Stay with us and we’ll explain how the DOE stage is crucial for narrowing down which design you choose, and we’ll go through these basic categories in detail.

There’s also a whole other way of doing designs, where you use software to create a bespoke design to your exact requirements. These are called optimal designs, and it’s a topic for another day. 

A single DOE design type may not cover your whole DOE campaign

DOE’s repeated iterations means that you learn as you go. They can move you rapidly from your initial “thought experiment” to optimized conditions and robust data. 

Which means that you don’t have to design a single experiment—or pick a single DOE design—that will answer all your questions. DOE is best used sequentially. Each iteration should take you closer to your goal. 

DOE designs are suited to different stages of the DOE process

Instead of trying to fit your DOE campaign into a single experiment, think of the DOE process as a collection of sub-experiments. And for each sub-experiment, you might use a different design—as every type of DOE design at each step is intimately linked to a phase in the DOE campaign. 

This means that the goal per DOE campaign stage should guide your choice of design:

• For the screening stage, you’ll need a design to help i dentify important factors and interactions.
• For the refinement and iteration stage, you’ll need a design to help s earch for the optimum value in the potential range of factors.
• For the optimization stage, you’ll need a design to help c reate a high-quality predictive model to infer optimal conditions.
• For assessing robustness, you’ll need a design to help determine the extent to which the system is sensitive to changes in your factor levels.

Some DOE designs lend themselves to achieving the goals of more than 1 stage at a time, such as screening and optimization. Often, however, this isn’t possible. The choice depends on your factors and the system’s complexity. 

The design you choose will inform your analysis

Too often, analysis is an afterthought. You can spend ages trying to figure out which model fits.

The beauty of DOE is that by choosing a DOE design, you have also chosen the type of analysis you will do. By picking the right design in the beginning, you’ll be saving yourself tons of work and grief when it comes to the analysis.

Designs for later stages involve making trade-offs

As your campaign progresses, the DOE design types involve investing more experimental effort to answer more detailed questions. So, you may have to leave some things for later. You need to make a choice. 

Do you need an answer now? Can you do a couple of iterations? Some DOE designs allow you to compress some stages . B ut of course, this results in trade-offs and limitations. Though you’ll find that DOE software will often prevent you from making certain choices that your design does not permit.

3 main types of DOE designs, explained

At any stage in your DOE campaign, you could take your pick from several designs, depending on your assumptions, goals, available run numbers, and so on. 

But when you’re new to DOE, there’s nothing worse than having too many choices. Instead of covering all the designs in detail, we’ll start you off by covering the most commonly used and important DOE designs (Figure 1).

Figure 1:  Common types of DOE design, the stage they’re linked to, plus when they are most useful

1. Space filling designs

DOE stage they’re linked to: Scoping

When to use them: Space filling designs are useful if you do not have much prior knowledge of your system, you want to investigate the system more broadly, or find a starting point for future optimization during pre-screening.

What they’re useful for: Space filling designs investigate factors at many different levels, without making any assumptions about the structure of the space, or the type of model. This means that you lose the efficiency and some statistical properties of classical DOE designs, such as factorials. 

2. Factorial designs

DOE stages they’re linked to: Screening, Refinement and Iteration

When to use them: You typically use factorial designs in the early stages of a DOE campaign: Screening, plus iteration and refinement. They’re helpful when you’re exploring a lot of factors in outline.

What they’re useful for: You can use factorial designs with any type of factor. Though it’s worth keeping the number of levels small so you can explore more factors. With continuous factors, for example, you’d normally stick to 2 levels, representing the maximum and minimum of an interval of interest. 

Single central points in factorial designs

Many factorial designs add a single central point for each factor to help determine whether there is curvature. This can help you better understand the experimental space.

For example, imagine that we’re growing a crop of strawberries. By adding a single central point, it can guide you to the best (mean) yield, rather than an incorrect one (Figure 2). 

Full factorial vs fractional factorial designs

There are also two main types of factorial design: Full and fractional. 

Full factorial designs

Full factorial designs investigate all possible combinations of factors and levels. So, you can determine main effects and any order of interaction. Full factorial designs, however, involve a large number of runs, increasing exponentially as the number of factors increase. 

Full factorial designs are often most appropriate when screening has identified a few important factors to optimize, or when using liquid handling automation affording an increase in throughput. 

Look to Figure 3 for an example in action: It shows how a full factorial works through every combination of every factor that screening suggested could influence the yield of strawberries. 

Figure 3: A full factorial design to uncover the best conditions for yield in a strawberry crop

Fractional factorial designs

When factors or levels increase, full factorial designs can become infeasible—even in sophisticated, high-capacity facilities. You can use fractional factorial designs when you have a large number of factors to screen, or where resources are limited.

A fractional factorial design takes a rational sample of the experimental landscape to provide a balanced, structured design that generates explanatory and predictive models. 

Fractional factorial designs assume that while there may be many effects, only a few are important. They also assume that the lower order effect. In other words, interactions between 2 factors—are more common and more influential than higher order effects (typically, interactions between more than 3 factors). 

So, the design doesn’t include the high-order interactions with each other, which drastically reduces run numbers. 

Fractional factorial DOE is not, however, suitable for sophisticated modeling. 

Let’s use growing strawberries as our example: Figure 4 shows how a fractional factorial design still covers a lot of ground in only half the runs, which saves time and resources.

Figure 4: A fractional factorial design for growing the yield of the same strawberry crop

Assessing fractional factorial designs with aliasing in mind

Every run is required to distinguish some combination of factors. So naturally, by taking runs out of a factorial, as you would with fractional factorials, you inevitably end up aliasing between 2 effects. Translation: The 2 effects cannot be distinguished from each other.

Aliasing forms the foundation of fractional factorial designs—and it’s something that you have to bear in mind when assessing them. When analyzing data you can encounter a situation where you can’t tell whether, for example, an interaction between 2 or 3 factors causes a particular effect. You either have to use your existing knowledge to decide or, if necessary, do more experiments.

Aliasing is particularly useful during screening to explore more factors without incurring a huge experimental cost. If you assume that higher order effects are likely to be less important than lower order effects (main effects and interactions between 2 factors), then you can alias higher order interactions with each other and substantially reduce the number of runs. 

Ultimately, assessing a particular fractional factorial is about deciding whether you can determine any of the particular higher order effects that you think might be interesting.

Assessing factorial designs with power and resolution

When you’re assessing your design, it’s important to think about power: Your ability to tell any effects apart from random noise.

It’s also important to think about resolution. 

Imagine that you’re looking at a high-resolution picture. It’s so clear, it’s easy to distinguish pixels that are sitting next to each other. Now, imagine you're looking at the same picture with a much lower resolution. It’s grainy, and it’s impossible to identify one pixel from the next. 

The same problems of having “low resolution” will apply to your DOE design. As when it comes to fitting a model to your data, if your DOE has a really low resolution, you won’t be able to tell the difference between effect a) and effect b). Whereas if you have a “high resolution”, distinguishing between one effect and another is easy. In short, resolution is all about assessing your design by how well you can tell different effects apart. 

You normally “grade” resolution with a number. This number tells you what “order”—or in non “DOE speak”, the size—of interactions you can distinguish within a particular effect. In other words, whether you can tell one 2-factor from another 2-factor interaction. 

3. Response surface methodology (RSM) designs

DOE stage they’re linked to: Optimization, Robustness

When to use them: Typically best for the optimization and robustness stages, you can use Response surface methodology (RSM) designs, also called specific names, such as Box-Behnken or central composite designs, if you detect significant factors during screening that display curvature (Figure 2).

In reality, there are nuances to the different types of RSM designs. Though some types of RSM designs can be thought of as full factorial designs across 2 levels for each factor, with center and axial points to sample additional levels without needing to do a full factorial across all levels. 

So, if you used a 2-level full factorial design during the refinement and iteration stage, you only need to add the axial points and replicated center points to achieve an RSM design.

This translates into a small amount of additional experimental work and can be a useful pattern when iterating from stage to stage of your DOE campaign. 

What they’re useful for: RSM designs can be applied to many kinds of factors. In general, however, RSM designs are not applied to categorical and discrete factors, as the experimental effort, or in other words, the number of runs you need, can become very expensive.

Optimizing your response with RSM designs

RSM designs allow you to build a predictive model of your system’s response surface. You can then use the predictive model to find the factor settings or region that will optimize your response. 

Check out Figure 5: If your design space is a mountain, the optimum response may be the peak. You are searching for the ideal convergence of factors that shows us where the peak is on this particular topographical map. 

Figure 5: Response surface methodology

RSM designs share many features with factorial designs. The assessment, however, differs. Instead of concepts like power and aliasing, which are relevant to determining which effects are real, RSM uses concepts relating to the errors when making predictions in the relevant design space. 

Pick the right design, unleash the power of DOE

It’s easy to get overwhelmed by all the design choices that most DOE software gives you. Most DOE software, especially our own, is also there to help guide you . They can also help you identify when technical restrictions apply and avoid making the wrong choice. 

And when in doubt? Remember to let your DOE campaign stage guide your design choices. It can also be helpful to think about how much work you can actually do—and find a design that fits your budget. Typically, if you have an unmanageable number of runs, you’ve likely picked the wrong design. 

And if you’re still in doubt? Remember that picking a design is better than no design! You’ve got to start somewhere.

This was an adaptation of a section from our DOE ebook: a biologist’s guide to Design of Experiments . Download it for free!

Dive deeper into Design of Experiments:

• Sign up for our free DOE training course to  learn the fundamentals in 6 minutes a day 
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Michael "Sid" Sadowski, PhD

Michael Sadowski, aka Sid, is the Director of Scientific Software at Synthace, where he leads the company’s DOE product development. In his 10 years at the company he has consulted on dozens of DOE campaigns, many of which included aspects of QbD.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental design is a structured approach used to conduct scientific experiments. It enables researchers to explore cause-and-effect relationships by controlling variables and testing hypotheses. This guide explores the types of experimental designs, common methods, and best practices for planning and conducting experiments.

Experimental Design

Experimental design refers to the process of planning a study to test a hypothesis, where variables are manipulated to observe their effects on outcomes. By carefully controlling conditions, researchers can determine whether specific factors cause changes in a dependent variable.

Key Characteristics of Experimental Design :

• Manipulation of Variables : The researcher intentionally changes one or more independent variables.
• Control of Extraneous Factors : Other variables are kept constant to avoid interference.
• Randomization : Subjects are often randomly assigned to groups to reduce bias.
• Replication : Repeating the experiment or having multiple subjects helps verify results.

Purpose of Experimental Design

The primary purpose of experimental design is to establish causal relationships by controlling for extraneous factors and reducing bias. Experimental designs help:

• Test Hypotheses : Determine if there is a significant effect of independent variables on dependent variables.
• Control Confounding Variables : Minimize the impact of variables that could distort results.
• Generate Reproducible Results : Provide a structured approach that allows other researchers to replicate findings.

Types of Experimental Designs

Experimental designs can vary based on the number of variables, the assignment of participants, and the purpose of the experiment. Here are some common types:

1. Pre-Experimental Designs

These designs are exploratory and lack random assignment, often used when strict control is not feasible. They provide initial insights but are less rigorous in establishing causality.

• Example : A training program is provided, and participants’ knowledge is tested afterward, without a pretest.
• Example : A group is tested on reading skills, receives instruction, and is tested again to measure improvement.

2. True Experimental Designs

True experiments involve random assignment of participants to control or experimental groups, providing high levels of control over variables.

• Example : A new drug’s efficacy is tested with patients randomly assigned to receive the drug or a placebo.
• Example : Two groups are observed after one group receives a treatment, and the other receives no intervention.

3. Quasi-Experimental Designs

Quasi-experiments lack random assignment but still aim to determine causality by comparing groups or time periods. They are often used when randomization isn’t possible, such as in natural or field experiments.

• Example : Schools receive different curriculums, and students’ test scores are compared before and after implementation.
• Example : Traffic accident rates are recorded for a city before and after a new speed limit is enforced.

4. Factorial Designs

Factorial designs test the effects of multiple independent variables simultaneously. This design is useful for studying the interactions between variables.

• Example : Studying how caffeine (variable 1) and sleep deprivation (variable 2) affect memory performance.
• Example : An experiment studying the impact of age, gender, and education level on technology usage.

5. Repeated Measures Design

In repeated measures designs, the same participants are exposed to different conditions or treatments. This design is valuable for studying changes within subjects over time.

• Example : Measuring reaction time in participants before, during, and after caffeine consumption.
• Example : Testing two medications, with each participant receiving both but in a different sequence.

Methods for Implementing Experimental Designs

• Purpose : Ensures each participant has an equal chance of being assigned to any group, reducing selection bias.
• Method : Use random number generators or assignment software to allocate participants randomly.
• Purpose : Prevents participants or researchers from knowing which group (experimental or control) participants belong to, reducing bias.
• Method : Implement single-blind (participants unaware) or double-blind (both participants and researchers unaware) procedures.
• Purpose : Provides a baseline for comparison, showing what would happen without the intervention.
• Method : Include a group that does not receive the treatment but otherwise undergoes the same conditions.
• Purpose : Controls for order effects in repeated measures designs by varying the order of treatments.
• Method : Assign different sequences to participants, ensuring that each condition appears equally across orders.
• Purpose : Ensures reliability by repeating the experiment or including multiple participants within groups.
• Method : Increase sample size or repeat studies with different samples or in different settings.

Steps to Conduct an Experimental Design

• Clearly state what you intend to discover or prove through the experiment. A strong hypothesis guides the experiment’s design and variable selection.
• Independent Variable (IV) : The factor manipulated by the researcher (e.g., amount of sleep).
• Dependent Variable (DV) : The outcome measured (e.g., reaction time).
• Control Variables : Factors kept constant to prevent interference with results (e.g., time of day for testing).
• Choose a design type that aligns with your research question, hypothesis, and available resources. For example, an RCT for a medical study or a factorial design for complex interactions.
• Randomly assign participants to experimental or control groups. Ensure control groups are similar to experimental groups in all respects except for the treatment received.
• Randomize the assignment and, if possible, apply blinding to minimize potential bias.
• Follow a consistent procedure for each group, collecting data systematically. Record observations and manage any unexpected events or variables that may arise.
• Use appropriate statistical methods to test for significant differences between groups, such as t-tests, ANOVA, or regression analysis.
• Determine whether the results support your hypothesis and analyze any trends, patterns, or unexpected findings. Discuss possible limitations and implications of your results.

Examples of Experimental Design in Research

• Medicine : Testing a new drug’s effectiveness through a randomized controlled trial, where one group receives the drug and another receives a placebo.
• Psychology : Studying the effect of sleep deprivation on memory using a within-subject design, where participants are tested with different sleep conditions.
• Education : Comparing teaching methods in a quasi-experimental design by measuring students’ performance before and after implementing a new curriculum.
• Marketing : Using a factorial design to examine the effects of advertisement type and frequency on consumer purchase behavior.
• Environmental Science : Testing the impact of a pollution reduction policy through a time series design, recording pollution levels before and after implementation.

Experimental design is fundamental to conducting rigorous and reliable research, offering a systematic approach to exploring causal relationships. With various types of designs and methods, researchers can choose the most appropriate setup to answer their research questions effectively. By applying best practices, controlling variables, and selecting suitable statistical methods, experimental design supports meaningful insights across scientific, medical, and social research fields.

• Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research . Houghton Mifflin Company.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference . Houghton Mifflin.
• Fisher, R. A. (1935). The Design of Experiments . Oliver and Boyd.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics . Sage Publications.
• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences . Routledge.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Types of Design of Experiments

Determining which type of DOE to use depends largely on: - Experimental goal; - Cost and resource constraints (or any practical limitations)

There are generally two categories of DOE: classical and modern designs.

Classical designs are mostly used to introduce DOE concepts, whereas modern designs are mostly used by industry practitioners in carrying out experiments.

Some examples of classical/textbook designs:

• Full factorial designs
• Fractional factorial designs (Screening designs)
• Response surface designs
• Mixture designs
• Taguchi array designs
• Split plot designs

Examples of custom/modern/computer-generated designs:

• Definitive screening design
• Custom designs

Full Factorial Designs

Trials are run at all possible combinations of factor settings. The sample size is the product of the numbers of levels of the factors. For example, a factorial experiment with a two-level factor, a three-level factor and a four-level factor has 2 x 3 x 4 = 24 runs.

Full factorial designs are often too expensive to run, since the sample size grows exponentially with the number of factors.

They are typically used when the number of factors and levels are small, and when we want all possible interaction information. Hence the most commonly used factorial designs are 2 k full factorials.

Screening Designs

Screening designs are among the most popular designs for industrial experimentation. They’re typically used in initial stages of experimentation to narrow down the long list of potentially important factors and interactions to only a few important effects.

Screening designs usually require fewer experimental runs than other designs. The experiments are small and efficient, involving many factors.

Some classical screening designs include fractional factorial designs, Plackett-Burman, Cotter and mixed-level designs.

Goal: Used for exploratory purposes (for example, to identify a handful of important effects).

Response Surface Designs

Response surface experiments are typically used in the latter stages of experimentations when the important factors have been identified. It usually involves a small number (generally two to eight) of continuous factors that have been identified as active.

It is used to model the curvature in the relationship between the factors and the response. It allows us to find settings of our factors to minimize or maximize a response or to hit a specific target.

In order to estimate the curvature, the design requires at least three levels for the factors. As a result, response surface designs can get extremely large unless the number of factors is limited.

Goal: To optimize processes by developing a predictive model of the relationship between the factors and the response.

Mixture Designs

Mixture designs are used when factors are interdependent, and when each component in a mixture is dependent upon the settings of other component settings. For example, in the case of stainless steel made up of Fe, Cu, Cr and Ni, the relative proportions of these components contribute to the properties of resulting steel.

A factor's value is its proportion of the mixture, which falls between zero and one. Mixture experiments have three or more factors with the sum of the factor proportions equal to one (100%). Hence, its experimental space is typically triangular and forms a simplex.

Some types of mixture designs include simplex centroid, simplex lattice, ABCD design and extreme vertices. Learn more .

Goal: Optimize recipe for a mixture of several ingredients.

Split Plot Designs

Split plot designs are typically used when an experiment involves hard-to-change variables, i.e., temperature of an industrial oven or the location of a cornfield. Traditional randomized experiments require factors to be tested for each run, which is impractical in this case.

Split plot designs is a blocked experiment, having the blocks serve as experimental units for a subset of factors. In split plot experiments, a treatment is applied to more than one experimental unit because a factor(s) is associated with batch processing, or it is hard or costly to change. 

As a result, split plot experiments are more practical to be carried out in the industrial world.

Some types of split plot designs include split-split plot design (nested relationship) and strip plot design (cross relationship).

Goal: Enable experiments to be carried out even with presence of hard-to-change variables.

Taguchi Array Designs

Taguchi array designs are used to identify signal factors (or control factors), which minimizes the effect of noise factors that are typically difficult or expensive to control.

It is carried out based on Taguchi’s inner and outer array approach. Inner array: control factors to find optimum settings. Outer array: noise factors looking at how response behaves in wide range noise conditions.

The alternative method used is combined arrays, which are generally more cost-effective and informative than Taguchi arrays.

Goal: To ensure consistency in output, by finding control factor settings that generate acceptable responses despite natural environmental and process variability.

Definitive Screening Designs

Definitive screening designs are mostly used in the earliest stages of experimentation. Unlike traditional screening designs, which usually require follow-up experimentation to resolve ambiguity if there’s any two-factor interaction, definitive screening design can reliably accomplish the task of screening even if there are a couple of second-order effects.

This is a highly efficient design that avoids model ambiguity and enables us to identify important factors quickly and efficiently. It estimates main effects and quadratic effects, and when only a few of the factors are important, you can also estimate some of the interaction effects.

Goal: To study many factors at once and identify the most important factors. Sometimes also used for optimization.

Custom Designs

Custom designs are used in almost any experimental situations, including factor screening and optimizations. They’re designs of experiments that are customized to solve our problem.

Custom designs do a better job of achieving our experimental goal in just one experiment. For example, if we want to study four factors, and our experimental goal is optimization. Three of the factors are continuous, and the fourth is a two-level categorical variable. Due to budget constraints, we’re limited to conduct only 14 trials. None of the existing traditional designs fits the bill, but we can use custom designs to solve our problem.  See more examples .

Custom design is the more efficient method in experimentations, offering far more flexibility to researchers. Most practitioners use custom designs in their work to save time and cost.

Using JMP software, we can easily construct a design that fits our use case and scenario best. 

Goal: Construct optimal designs that fit our needs.