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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Dissertations / Theses on the topic 'Design of Experiments (DOE)'

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Guerreiro, Luís Filipe Costa. "Automatic drilling improvement and standardization by design-of-experiments (DOE)." Master's thesis, Universidade de Évora, 2019. http://hdl.handle.net/10174/25737.

Choi, Paul Koon Ping. "The use of design of experiments (DOE) : time for company management to decide." Thesis, University of the West of Scotland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556176.

Khaddaj-Mallat, Chadi. "Design of experiments approach to the flooding of damaged ships." Ecole centrale de Nantes, 2010. http://www.theses.fr/2010ECDN0024.

Johansson, Robin. "Structural optimization of electronic packages using DOE." Thesis, KTH, Hållfasthetslära, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285859.

Verlaan, Eric, Wouter Hendriksen, Rob Meulenbroek, and Prie Devlin du. "Design of Experiments (DOE) for Product and Process Improvements - 130: A Phenolic Syntan Case Study." Verein für Gerberei-Chemie und -Technik e. V, 2019. https://slub.qucosa.de/id/qucosa%3A34176.

Chini, Marco. "Sviluppo di nuove metodologie di calibrazione per motori da competizione con tecniche di Design of Experiments." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18647/.

Lindberg, Tomas. "An application of DOE in the evaluation of optimization functions in a statistical software." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39507.

Chantarat, Navara. "Modern design of experiments methods for screening and experimentations with mixture and qualitative variables." Columbus, OH : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1064198056.

Farias, Marcelo Fernandes. "Determinação da influência de parâmetros de processo de forjamento a quente utilizando DOE (projeto de experimentos)." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/170979.

Clay, Stephen Brett. "Characterization of Crazing Properties of Polycarbonate." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/28648.

Sandqvist, Wedin Emma. "Optimization of Acidic Degradation of Hyaluronic Acid using Design of Experiments." Thesis, Linköpings universitet, Teknisk biologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156273.

Smeliková, Lenka. "Kontrola kvality pájeného spoje a Design of Experiments u strojního pájení vlnou." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220991.

Amanna, Ashwin Earl. "Statistical Experimental Design Framework for Cognitive Radio." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77331.

Henriques, Francisco José da Silva. "O uso do DOE em conjunto com FTA no desenvolvimento e melhoria de projetos inovadores." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/263938.

Westbeld, Julius. "Investigation of support structures of a polymer powder bed fusion process by use of Design of Experiment (DoE)." Thesis, KTH, Lättkonstruktioner, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243867.

Scheffler, Liziane da Luz Seben. "Estudo exploratório de extração de celulose a partir de resíduos vegetais do processo produtivo de conserva de palmito (Archontophoenix alexandrae)." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/35616.

Sabová, Iveta. "Plánovaný experiment." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-231981.

Nilsson, Marcus, and Johan Ruth. "SPC and DOE in production of organic electronics." Thesis, Linköping University, Department of Science and Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6240.

At Acreo AB located in Norrköping, Sweden, research and development in the field of organic electronics have been conducted since 1998. Several electronic devices and systems have been realized. In late 2003 a commercial printing press was installed to test large scale production of these devices. Prior to the summer of 2005 the project made significant progress. As a step towards industrialisation, the variability and yield of the printing process needed to bee studied. A decision to implement Statistical Process Control (SPC) and Design of Experiments (DOE) to evaluate and improve the process was taken.

SPC has been implemented on the EC-patterning step in the process. A total of 26 Samples were taken during the period October-December 2005. An - and s-chart were constructed from these samples. The charts clearly show that the process is not in statistical control. Investigations of what causes the variation in the process have been performed. The following root causes to variation has been found:

PEDOT:PSS-substrate sheet resistance and poorly cleaned screen printing drums.

After removing points affected by root causes, the process is still not in control. Further investigations are needed to get the process in control. Examples of where to go next is presented in the report. In the DOE part a four factor full factorial experiment was performed. The goal with the experiment was to find how different factors affects switch time and life length of an electrochromic display. The four factors investigated were: Electrolyte, Additive, Web speed and Encapsulation. All statistical analysis was performed using Minitab 14. The analysis of measurements from one day and seven days after printing showed that:

- Changing Electrolyte from E230 to E235 has small effect on the switch time

- Adding additives Add1 and Add2 decreases the switch time after 1 and 7 days

- Increasing web speed decreases the switch time after 1 and 7 days

- Encapsulation before UV-step decreases the switch time after 7 days

Rådberg, Malin. "Design of Experiment for Laser cutting in Superalloy Haynes 282." Thesis, Karlstads universitet, Science, Mathematics and Engineering Education Research (SMEER), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-44516.

Nelson, Benjamin D. "Using Design of Experiments and Electron Backscatter Diffraction to Model Extended Plasticity Mechanisms In Friction Stir Welded AISI 304L Stainless Steel." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2582.

Knob, Jan. "Pěnění fermentačních zbytků při vakuovém odpařování." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-378403.

Andersson, David. "Multivariate design of molecular docking experiments : An investigation of protein-ligand interactions." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-35736.

Bredda, Eduardo Henrique. "Estudo comparativo e otimização da quantidade de ômega 3 e ômega 6 produzido pelas microalgas nannochloropsis gaditana e dunaliella salina /." Guaratinguetá, 2019. http://hdl.handle.net/11449/183502.

Tosto, Francesco. "Investigation of performance and surge behavior of centrifugal compressors through CFD simulations." Thesis, KTH, Mekanik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226159.

Hizli, Cem. "Thermal Optimization of Veo+ Projectors (thesis work at Optea AB) : Trying to reduce noise of the Veo+ projector by DOE (Design of Experiment) tests to find anoptimal solution for the fan algorithm while considering the thermal specifics of the unit." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-10382.

Holec, Tomáš. "Plánovaný experiment." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2016. http://www.nusl.cz/ntk/nusl-254455.

Venturini, Giacomo. "Design of experiment analysis of air filter performance for helicopter applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

Jakob, Marius. "Methode zur Gestaltung anwendungsabhängiger Mitnehmerverbindungen: Leichtbau und Steigerung der Tragfähigkeit durch dünnwandige Profilwellen." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34105.

Fogliatto, Aloysio Arthur Becker. "Influência dos parâmetros do processo MIG/MAG com curto-circuito controlado sobre a geometria do cordão de solda." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/75921.

Borunský, Tomáš. "Optimalizace procesu tlakového lití VN přístrojových transformátorů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228552.

Laiate, Juliana. "Estudo do processo de cultivo da microalga chlorella minutíssima e caracterização termoquímica de sua biomassa para aplicação em gaseificação." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/157246.

Zavoli, Chiara. "Applicazione del metodo Design of Experiment per l’analisi del ciclo di decontaminazione con H2O2 nell’industria farmaceutica." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

Haase, Dirk. "Ein neues Verfahren zur modellbasierten Prozessoptimierung auf der Grundlage der statistischen Versuchsplanung am Beispiel eines Ottomotors mit elektromagnetischer Ventilsteuerung (EMVS)." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2005. http://nbn-resolving.de/urn:nbn:de:swb:14-1129553378853-30864.

Yurtseven, Saygin. "Analysis Of The Influence Of Non-machining Process Parameters On Product Quality By Experimental Design And Statistical Analysis." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1026863/index.pdf.

Nwagoum, Idriss Chatrian. "aerodynamic performance improvement of a twin scroll turbocharger turbine using the design of experiments method." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

Panieri, Marco. "Ottimizzazione mediante progetto dell'esperimento del processo di produzione di idrossiapatite drogata con magnesio." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

Abdalrahman, Rzgar. "Design and analysis of integrally-heated tooling for polymer composites." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/4753.

Fukuda, Isa Martins. "Desenvolvimento e otimização de protetores solares empregando os conceitos de qualidade por design (QbD) e tecnologia analí­tica de processos (PAT)." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/9/9139/tde-12112018-145821/.

Haase, Dirk. "Ein neues Verfahren zur modellbasierten Prozessoptimierung auf der Grundlage der statistischen Versuchsplanung am Beispiel eines Ottomotors mit elektromagnetischer Ventilsteuerung (EMVS)." Doctoral thesis, Technische Universität Dresden, 2004. https://tud.qucosa.de/id/qucosa%3A24582.

Berglund, Anders. "Criteria for Machinability Evaluation of Compacted Graphite Iron Materials : Design and Production Planning Perspective on Cylinder Block Manufacturing." Doctoral thesis, KTH, Industriell produktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-48430.

QC 20111121

Abbas, Manzar. "System-level health assessment of complex engineered processes." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37260.

Besirevic, Edin, and Anders Dahl. "Variance reduction of product parameters in wire rope production by optimisation of process parameters." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-63634.

BATISTA, NETO Leopoldo Viana. "Otimização do processo de disposição de filmes TiN e TiZrN em aço inoxidável utilizando planejamento experimental fatorial." Universidade Federal de Campina Grande, 2014. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/378.

Fu, Tingrui. "PP/clay nanocomposites : compounding and thin-wall injection moulding." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/24655.

Eghlio, Ramadan Mahmoud. "Laser net shape welding of steels." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/laser-net-shape-welding-of-steels(c5275bf1-ac62-4195-9d4e-61d1973d1b6f).html.

Ramesh, Dinesh. "The Role of Interface in Crystal Growth, Energy Harvesting and Storage Applications." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1752367/.

Record, Jonathan H. "Statistical Investigation of Friction Stir Processing Parameter Relationships." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd732.pdf.

Abtini, Mona. "Plans prédictifs à taille fixe et séquentiels pour le krigeage." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEC019/document.

Clifford, Dustin M. "Non-Conventional Approaches to Syntheses of Ferromagnetic Nanomaterials." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4205.

Park, Jangho. "Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91911.

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Definition of 3D Printing Parameters by the Design of Experiments to Characterise Carbon Fibre-Reinforced Polyamide

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• Gorka Unzueta 6 ,
• Jose Alberto Eguren 6 ,
• Aritz Esnaola 6 ,
• Jon Aurrekoetxea 6 &
• Itxaro Sukia 6  

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 206))

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This paper presents the application of an advanced quality management tool, the design of experiments (DOE), in order to characterise a new material (carbon fibre-reinforced polyamide) used in the 3D printing process. The study focuses on the definition of optimal 3D printing parameters, such as nozzle size, temperature, print speed, layer height and print orientation, to achieve desired mechanical properties. The results show that layer height and print orientation have a significant effect on mechanical properties and printing time. This study provides insights into the optimisation of 3D printing parameters for the production of carbon fibre-reinforced polyamide (PA-CF) parts with desired mechanical properties, which can have important applications in various industries, such as aerospace, automotive and medical devices.

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Eguren, J.A., Esnaola, A., Unzueta, G.: Modelling of an additive 3d-printing process based on design of experiments methodology. Quality Innovation Prosperity 24 (1), 128–151 (2020). https://doi.org/10.12776/QIP.V24I1.1435

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Department of Mechanics and Industrial Production, Mondragon Unibertsitatea, Arrasate, Spain

Gorka Unzueta, Jose Alberto Eguren, Aritz Esnaola, Jon Aurrekoetxea & Itxaro Sukia

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Unzueta, G., Eguren, J.A., Esnaola, A., Aurrekoetxea, J., Sukia, I. (2024). Definition of 3D Printing Parameters by the Design of Experiments to Characterise Carbon Fibre-Reinforced Polyamide. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_1

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Product Design Thesis Showcase

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We are thrilled to extend to you a special invitation to Montclair State University’s highly anticipated Product Design Thesis Showcase! This event promises to be an inspiring celebration of creativity, innovation, and the culmination of months of hard work by our talented product design students. Whether you’re an industry professional seeking fresh talent, a design enthusiast looking for inspiration, or simply curious about the future of product design, this event is sure to captivate and inspire you featuring groundbreaking concepts, captivating presentations, and an engaging interactive Q&A session.

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We cordially invite you to witness firsthand the remarkable talent and dedication of our senior Product Design students. Your presence will undoubtedly enhance this celebration of creativity and innovation.

• Product Design Senior Presentation
• May 6, 2024, 6:00 pm
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