experimental design essay example

15 Experimental Design Examples

Experimental design involves testing an independent variable against a dependent variable. It is a central feature of the scientific method .

A simple example of an experimental design is a clinical trial, where research participants are placed into control and treatment groups in order to determine the degree to which an intervention in the treatment group is effective.

There are three categories of experimental design . They are:

• Pre-Experimental Design: Testing the effects of the independent variable on a single participant or a small group of participants (e.g. a case study).
• Quasi-Experimental Design: Testing the effects of the independent variable on a group of participants who aren’t randomly assigned to treatment and control groups (e.g. purposive sampling).
• True Experimental Design: Testing the effects of the independent variable on a group of participants who are randomly assigned to treatment and control groups in order to infer causality (e.g. clinical trials).

A good research student can look at a design’s methodology and correctly categorize it. Below are some typical examples of experimental designs, with their type indicated.

Experimental Design Examples

The following are examples of experimental design (with their type indicated).

1. Action Research in the Classroom

Type: Pre-Experimental Design

A teacher wants to know if a small group activity will help students learn how to conduct a survey. So, they test the activity out on a few of their classes and make careful observations regarding the outcome.

The teacher might observe that the students respond well to the activity and seem to be learning the material quickly.

However, because there was no comparison group of students that learned how to do a survey with a different methodology, the teacher cannot be certain that the activity is actually the best method for teaching that subject.

2. Study on the Impact of an Advertisement

An advertising firm has assigned two of their best staff to develop a quirky ad about eating a brand’s new breakfast product.

The team puts together an unusual skit that involves characters enjoying the breakfast while engaged in silly gestures and zany background music. The ad agency doesn’t want to spend a great deal of money on the ad just yet, so the commercial is shot with a low budget. The firm then shows the ad to a small group of people just to see their reactions.

Afterwards they determine that the ad had a strong impact on viewers so they move forward with a much larger budget.

3. Case Study

A medical doctor has a hunch that an old treatment regimen might be effective in treating a rare illness.

The treatment has never been used in this manner before. So, the doctor applies the treatment to two of their patients with the illness. After several weeks, the results seem to indicate that the treatment is not causing any change in the illness. The doctor concludes that there is no need to continue the treatment or conduct a larger study with a control condition.

4. Fertilizer and Plant Growth Study

An agricultural farmer is exploring different combinations of nutrients on plant growth, so she does a small experiment.

Instead of spending a lot of time and money applying the different mixes to acres of land and waiting several months to see the results, she decides to apply the fertilizer to some small plants in the lab.

After several weeks, it appears that the plants are responding well. They are growing rapidly and producing dense branching. She shows the plants to her colleagues and they all agree that further testing is needed under better controlled conditions .

5. Mood States Study

A team of psychologists is interested in studying how mood affects altruistic behavior. They are undecided however, on how to put the research participants in a bad mood, so they try a few pilot studies out.

They try one suggestion and make a 3-minute video that shows sad scenes from famous heart-wrenching movies.

They then recruit a few people to watch the clips and measure their mood states afterwards.

The results indicate that people were put in a negative mood, but since there was no control group, the researchers cannot be 100% confident in the clip’s effectiveness.

6. Math Games and Learning Study

Type: Quasi-Experimental Design

Two teachers have developed a set of math games that they think will make learning math more enjoyable for their students. They decide to test out the games on their classes.

So, for two weeks, one teacher has all of her students play the math games. The other teacher uses the standard teaching techniques. At the end of the two weeks, all students take the same math test. The results indicate that students that played the math games did better on the test.

Although the teachers would like to say the games were the cause of the improved performance, they cannot be 100% sure because the study lacked random assignment . There are many other differences between the groups that played the games and those that did not.

Learn More: Random Assignment Examples

7. Economic Impact of Policy

An economic policy institute has decided to test the effectiveness of a new policy on the development of small business. The institute identifies two cities in a third-world country for testing.

The two cities are similar in terms of size, economic output, and other characteristics. The city in which the new policy was implemented showed a much higher growth of small businesses than the other city.

Although the two cities were similar in many ways, the researchers must be cautious in their conclusions. There may exist other differences between the two cities that effected small business growth other than the policy.

8. Parenting Styles and Academic Performance

Psychologists want to understand how parenting style affects children’s academic performance.

So, they identify a large group of parents that have one of four parenting styles: authoritarian, authoritative, permissive, or neglectful. The researchers then compare the grades of each group and discover that children raised with the authoritative parenting style had better grades than the other three groups. Although these results may seem convincing, it turns out that parents that use the authoritative parenting style also have higher SES class and can afford to provide their children with more intellectually enriching activities like summer STEAM camps.

9. Movies and Donations Study

Will the type of movie a person watches affect the likelihood that they donate to a charitable cause? To answer this question, a researcher decides to solicit donations at the exit point of a large theatre.

He chooses to study two types of movies: action-hero and murder mystery. After collecting donations for one month, he tallies the results. Patrons that watched the action-hero movie donated more than those that watched the murder mystery. Can you think of why these results could be due to something other than the movie?

10. Gender and Mindfulness Apps Study

Researchers decide to conduct a study on whether men or women benefit from mindfulness the most. So, they recruit office workers in large corporations at all levels of management.

Then, they divide the research sample up into males and females and ask the participants to use a mindfulness app once each day for at least 15 minutes.

At the end of three weeks, the researchers give all the participants a questionnaire that measures stress and also take swabs from their saliva to measure stress hormones.

The results indicate the women responded much better to the apps than males and showed lower stress levels on both measures.

Unfortunately, it is difficult to conclude that women respond to apps better than men because the researchers could not randomly assign participants to gender. This means that there may be extraneous variables that are causing the results.

11. Eyewitness Testimony Study

Type: True Experimental Design

To study the how leading questions on the memories of eyewitnesses leads to retroactive inference , Loftus and Palmer (1974) conducted a simple experiment consistent with true experimental design.

Research participants all watched the same short video of two cars having an accident. Each were randomly assigned to be asked either one of two versions of a question regarding the accident.

Half of the participants were asked the question “How fast were the two cars going when they smashed into each other?” and the other half were asked “How fast were the two cars going when they contacted each other?”

Participants’ estimates were affected by the wording of the question. Participants that responded to the question with the word “smashed” gave much higher estimates than participants that responded to the word “contacted.”

12. Sports Nutrition Bars Study

A company wants to test the effects of their sports nutrition bars. So, they recruited students on a college campus to participate in their study. The students were randomly assigned to either the treatment condition or control condition.

Participants in the treatment condition ate two nutrition bars. Participants in the control condition ate two similar looking bars that tasted nearly identical, but offered no nutritional value.

One hour after consuming the bars, participants ran on a treadmill at a moderate pace for 15 minutes. The researchers recorded their speed, breathing rates, and level of exhaustion.

The results indicated that participants that ate the nutrition bars ran faster, breathed more easily, and reported feeling less exhausted than participants that ate the non-nutritious bar.

13. Clinical Trials

Medical researchers often use true experiments to assess the effectiveness of various treatment regimens. For a simplified example: people from the population are randomly selected to participate in a study on the effects of a medication on heart disease.

Participants are randomly assigned to either receive the medication or nothing at all. Three months later, all participants are contacted and they are given a full battery of heart disease tests.

The results indicate that participants that received the medication had significantly lower levels of heart disease than participants that received no medication.

14. Leadership Training Study

A large corporation wants to improve the leadership skills of its mid-level managers. The HR department has developed two programs, one online and the other in-person in small classes.

HR randomly selects 120 employees to participate and then randomly assigned them to one of three conditions: one-third are assigned to the online program, one-third to the in-class version, and one-third are put on a waiting list.

The training lasts for 6 weeks and 4 months later, supervisors of the participants are asked to rate their staff in terms of leadership potential. The supervisors were not informed about which of their staff participated in the program.

The results indicated that the in-person participants received the highest ratings from their supervisors. The online class participants came in second, followed by those on the waiting list.

15. Reading Comprehension and Lighting Study

Different wavelengths of light may affect cognitive processing. To put this hypothesis to the test, a researcher randomly assigned students on a college campus to read a history chapter in one of three lighting conditions: natural sunlight, artificial yellow light, and standard fluorescent light.

At the end of the chapter all students took the same exam. The researcher then compared the scores on the exam for students in each condition. The results revealed that natural sunlight produced the best test scores, followed by yellow light and fluorescent light.

Therefore, the researcher concludes that natural sunlight improves reading comprehension.

See Also: Experimental Study vs Observational Study

Experimental design is a central feature of scientific research. When done using true experimental design, causality can be infered, which allows researchers to provide proof that an independent variable affects a dependent variable. This is necessary in just about every field of research, and especially in medical sciences.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

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• Experimental Design Essays

Experimental Design Essays (Examples)

1000+ documents containing “experimental design” .

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Experimental design employs comparison as its strategy.

Experimental design employs comparison as its strategy for the given research. It uses two groups, which the researcher uses for comparison purposes. These include the experimental group and a control group. The two groups used in a study have similarities, but the experimental group uses the independent variable, whereas the researcher the control group is not assignment of subjects to either control or experimental group because it is central to chance. Nevertheless, the researcher assigns cases to the two groups randomly. In order to determine the influence of the independent variable, investigators will measure the dependent variable, designated as scores, two times from both groups (Frankfort-Nachmias and Nachmias, 2008). In addition, researchers take a single measurement, the pretest, for all cases before introducing the independent variable in the experimental group. Moreover, they also take a second measurement, the posttest, for both cases after exposing the experimental group to the independent variable.….

Frankfort-Nachmias, C., Nachmias, D. (2008). Research methods in social sciences 7 ed. New

York: Worth Publishers.

Walker, W. (2005). The strengths and weaknesses of research designs involving quantitative methods. Journal of research in nursing, 10(5), 571-582.

Experimental Design Background of the

Dependent Variable 1: Strength of Expressed Opinions on Questionnaire. Independent Variable 2: Exposure to Indirect Intervention. Dependent Variable 2: Strength of Expressed Opinions on Questionnaire. Variables Direct Intervention -- Test subjects will be exposed to conversation at the dinner table directed to them and including them on three separate occasions one week apart about the importance of consuming alcohol responsibly and about the dangerous consequences of drinking irresponsibly. Indirect Intervention -- Test subjects will be exposed to modeled behavior of adults expressing concern over matters such as designating a non-drinking driver and avoiding excessive intoxication without any conversation about them or directed to them three separate occasions one week apart. Controls There will be a control condition in the post-test version of the experimental design. Specifically, the responses of test subjects will be compared to a comparable control group that is not exposed to the specified intervention. Manipulation Check The parents will be instructed to either engage in verbal….

Experimental Design for Hypothetical Research Study Recent

Experimental Design for Hypothetial Researh Study Reent researh has emerged whih suggests that the ingestion of hoolate may lead to improved ognitive funtion within the realm of memorization and retention of information. Establishing a onlusive link between ertain hemial omponents found in hoolate and the improvement of memory funtion would be a signifiant point of progress for medial siene, espeially when the impat of Alzheimer's disease, early-onset dementia and other memory-redution ailments on senior itizens is fully onsidered. By expanding on the work of Jones and Wilson (2011) -- who improved soring on math tests two hours after subjets ate hoolate -- it may be possible to identify the partiular enzymes released during digestion whih serve to alter fundamental aspets of memory. Researh published by Wong, Hideki, Anderson, and Skaarsgard (2009) -- whih suggests that the impat of hoolate on memory improvement ours more frequently for women -- an also be….

cited in the Introduction, as the subjects who ingested chocolate before testing showing marked improvements over their baseline scores, while the control group exposed to a placebo chocolate substitute returned results which were nearly identical to their baseline. More specifically, women tested higher than their baseline at each duration interval of chocolate ingestion, and the gains experienced by women were significantly higher (on a statistical basis) than those produced by men. In terms of the previously stated hypothesis, the fact that women were consistently observed to record higher test scores after eating chocolate, and that these improvements consistently outpaced that documented in their male counterparts, would appear to suggest a biological basis for the discrepancy. Additional research must be performed from a molecular analysis standpoint to determine if a link between naturally occurring enzymes in chocolate and hormones like estrogen and progesterone which occur predominately in women. The conclusion to be drawn from this experiment is that chocolate contains a particular chemical capable of interacting with the brain on a biological level to stimulate improved cognitive function relating to memory and retention of information. Furthermore, this phenomenon has been observed to occur more frequently and more powerfully in women, suggesting that a component of female biology such as certain hormones may be producing an exaggerated effect. The null hypothesis stated prior to the experiment has been rejected, as chocolate appears to offer genuine benefits for those seeking to improve their ability to memorize facts and retain information.

Experimental Designs

Experimentation is one of the common methods used in quantitative research. Premised on the positivist philosophy, an experiment is essentially conducted to investigate causal relationships between variables (Bryman, 2008). Indeed, this is one of the major strengths of experimental research compared to other types of studies -- it not only describes association between variables, but also explains causation between variables (Kothari, 2004). This essay describes the various components of an experimental method plan. The paper also explains threats to validity as well as nuances involved in interpreting results from an empirical study. An experimental design has four major components: participants, materials, procedures, and measures (Creswell, 2014). Participants denote the subjects from which the required data will be obtained. The participants section should describe the process of selecting and assigning the participants. This involves explaining whether random or non-random procedures will be used to select participants, whether the participants will be randomly….

Experimental Design Feasible Why or Why Not

experimental design feasible? Why or why not? • What suggestions can you make for future studies of the DARE program? The aims of DARE are long-term in nature, namely to encourage students to not abuse drugs over the course of their lifetimes. The only way to test this aim is to conduct a longitudinal study of a representative body of DARE graduates over at least a twenty-year period, to see if the intervention had a lasting effect upon their drug use habits. The control groups would be a group of students from similar demographics and geographical locations who did not have DARE or any other anti-drug program in their schools and a group of students who experienced an anti-drug education intervention substantially different than DARE. The selection of students would have to be balanced in terms of factors such as race, gender, and neighborhood, given that graduates of DARE programs might….

Experimental Design for a Study

goal of the research is to conduct an assessment of probable issues relating to hiring a deputy director for the organization, which has operated without an actual office manager for a year. Consequently, the research objective is to identify positive qualities that an individual must possess to be considered for the vacant position of an office manager. In light of the current situation, acquiring a manager with positive qualities is the objective for the study because of the need to lessen the negative results of change in management and promote employees' receptiveness of the new manager. Experimental Design This study will entail conducting an experiment, which involves manipulating at least one independent variable and observing the impact on certain results i.e. dependent variable. The experiment will be conducted in the field because the study involves human subjects. Given these considerations, the most suitable experimental design is true experiment, which entails random….

Shaughnessy, J.J., Zechmeister, E. & Zechmeister, J. (2003).Research methods in psychology (6th ed.). New York, NY: Mc-Graw Hill.

Sommer, B. (n.d.). Types of Experiments. Retrieved from The Regents University of California -- Davis Campus website:  http://psychology.ucdavis.edu/sommerb/sommerdemo/experiment/types.htm

Featuring a Quantitative Experimental Design Related Criminal

featuring a QUANTITATIVE experimental design related criminal justice security management. Attach article ( a hyperlink article) posting. Please answer questions: Overview: Provide an overview study ( -write abstract; words). Confidence in the criminal justice system, by David Indermaur and Lynne oberts Indermaur and oberts (2009) commence by arguing the importance of the judicial system within any country, especially a developed one, where there is ongoing pressure to improve the quality of the criminal justice system. Throughout the past recent years then, various efforts have been made across the countries to reform and modernize the criminal justice system. The two authors as such strive to analyze these efforts and conclude on their effectiveness, based on the analysis of the confidence revealed by the people in the criminal justice system. In this examination, emphasis is placed on the reforms implemented in the UK and the confidence of the people in the criminal justice….

Indermaur, D., Roberts, L. (2009). Confidence in the criminal justice system. Trends and Issues in Crime and Criminal Justice. November edition.

Ball-And-Sock Experiment the Experimental Design I Altered

Ball-And-Sock Experiment he Experimental Design I altered the experiment slightly to help make sure that it would demonstrate the issues it was supposed to demonstrate without adding extraneous variables. Specifically, I was concerned that if I walked around all day with a sock on my hand, every person who interacted with me would ask about my sock and that would get in the way of the main point of simply forcing me to not to rely on my dominant hand in my daily routine. Likewise, there seemed to be no point to introducing the variable of self-consciousness since left-handed people don't usually wall around self-conscious and worried what everybody else thinks about their being left-handed. So, I simply wrapped the fingers of my right hand in some first-aid gauze to make it impossible to use them and then I wrapped an Ace bandage around my hand and wrist so that in….

The first thing I noticed was that the most ordinary chores, such as brushing my teeth and accomplishing other personal hygiene tasks were extremely difficult and took much longer. On one hand, that represented an inherent limitation in the experimental design, because those issues do not ordinarily affect anybody who has always been left-handed; they would only be issues for people who have lost the use of their dominant hands and not anybody who has been left-handed since birth. However, from the perspective of a manager in business, it certainly is relevant to understanding the challenges of the thousands of injured military veterans whose lives have been changed by the loss of limbs in Iraq or Afghanistan since 2003 and 2011, respectively. However, I did immediately notice that my toothbrush is shaped for the right hand and that I cannot ever remember even seeing a toothbrush labeled "left-handed" in the drug store or supermarket. Possibly the most significant problem I encountered was that I was completely unable to drive my car even though it is an automatic transmission. The transmission shifter is on my right side and requires pushing a button with my thumb. I realized that learning how to drive must be that much more difficult for left-handed people because it probably is easier to use your dominant hand on the gear shifter.

In principle, the experiment reinforced how much most of us live our lives totally oblivious to many of the challenges faced by others. For example, males typically do not appreciate that when a female speaks in a group situation in the workplace, she might have concerns about the attitude of others (especially her superiors) about the competence of women, or about whether being proactive or assertive might be considered negatives when they would not be seen that way if she were male. Similarly, a Caucasian manager who lacks cultural sensitivity might not consider that an African-American or Hispanic coworker might worry that his colleagues think he might not have earned his position by merit and that he might have to work harder (and be more careful to avoid mistakes or about admitting to encountering difficulty with a task) to avoid reinforcing prejudicial assumptions that he received his position (or his educational degree) partly because of affirmative action.

In fact, the more I thought about it, the more I started considering that there might have been a point to the ridiculous aspect of the ball-and-sock design after all: because when I walk into a classroom or a job interview, the last thing on my mind is that the person might be making pejorative judgments about me as soon as they seem me and before I open my mouth. I realized that for my minority colleagues, that might not be the case: they might walk into every room feeling as though they have a sock and ball attached to their hands.

Application of Experimental Design

limiting a researcher's view of the problem are situational factors that can skew the results of her experiment, i.e., effects of pretesting, social threats, and group differences (Trochim, 2008, 188). External factors, such as possible sample size, can limit even the type of testing available to the researcher. As such, researchers have come up with a number of different types of designs over the years. This essay will compare and contrast two of these; experimental and quasi-experimental designs. "Experimental designs are often touted as the most rigorous of all research designs" (Trochim, 2008, 186). hat is so rigorous about them is that they are the strongest type of design in regards to their internal validity (Trochim, 2008, 186). This is because the basic form of the experimental design uses random assignment, or chance to group participants. In effect, this makes the two groups, if selected from the same sample, basically….

Why would a researcher, who values internal validity, then choose a quasi-experimental design, that specifically lacks in internal validity? The reason is that experimental designs are not always the most effective. They're subject to social threats of internal validity, and have difficulties with external validity (Trochim, 2008, 188). An experimental design is "intrusive and difficult to carry out in most real-world contexts," and is basically an "artificial situation" created to "assess [a] causal relationship with high-internal validity." Resultingly, there are difficulties in generalizing the findings to the real world.

Alternatively, certain types of quasi-experimental designs are some "of the most intuitively sensible designs around" (Trochim, 2008, 210). This is because random selection is often not logical or possible. For example, say we wanted to understand the effects of a certain type of treatment on both developmentally disabled adults and adults that were not developmentally disabled. Random selection, or an experimental design, would be impossible here, because we already have our two groups; they're dictated by the difference.

The basic reasons one might choose an experimental design or a quasi-experimental design has to do with, I think, resources, and what exactly is being tested. I feel that there is no design that is "better" or "worse" -- applying those labels would not make any sense at all here, because every experiment has a different aim. What is good for one experiment may be bad for another. The most important thing, I feel, is simply to have an understanding of both, and the ability to employ each when necessary.

Evaluating Design Choice and Threats to Validity in a Quasi-Experimental Design

Threats to Validity in a Quasi-Experimental Design Evaluating design choice: Walk Texas! The research study by Bartholomew (et al. 2008) entitled "Walk Texas! 5-A-Day intervention for women, infant, and children (WIC) clients: A quasi-experimental study" is defined as quasi-experimental because it lacks a formal control group. The purpose of the study was to determine an intervention designed to improve the eating habits of low-income WIC clients. The participants were "primarily native Spanish speaking, Hispanic women, of low educational level" (Bartholomew 2008: 297). The study "utilized a pre-test post-test quasi-experimental design, with two intervention and two comparison clinics that were matched for size and ethnicity" (Bartholomew 2008: 297). The comparison clinics served as an informal control although participation in the experimental and control groups was not randomized, as would be the case in a true experimental study. The purpose of the experiment was to see if low-income women who make use of the….

Bartholomew, J.B. (et al. 2008). Walk Texas! 5-A-Day intervention for Women, Infant, and children (WIC) clients: A quasi-experimental study. Journal of Community Health, 33:297 -- 303. DOI 10.1007/s10900-008-9103-y

Effect of TV Adverts on Children Using Quasi Experimental Design

Quasi-Experimental Design on the Effect of TV Adverts on Children This study carries out the evaluation of a research titled "A quasi-experiment assessing the effectiveness of TV advertising directed to children" (Goldberg, 1990 p 445). The paper examines the extent the research hypotheses have been able to address the study. The paper also examines the research dependent variables and independent variables. Moreover, the study investigates the extent the author has adhered to both external and internal validity for the research. esearch question the study Addresses Goldberg, (1990) carries out the experimental research to investigate the potential impact of television advertising on children. Although, the author does not provide the research questions, nevertheless, the author tests two hypotheses using the quasi-experiment to assess the effectiveness of television advertising that has been directed to children. ationale for the study The rationale of the study is to assess whether children exposed to higher level of television advertisement can….

Goldberg, N. (1990). A Quasi-experiment Assessing the Effectiveness of TV Advertising directed to Children. Journal of Marketing Research JMR, 27 (4): 445

Khandker, Shahidur R., et al. (2010). Handbook on Impact Evaluation: Quantitative Methods and Practices, World Bank, Washington, D.C: 53-103.

Morgan, G. A. (2000). Quasi-Experimental Designs. Journal of the American Academy of Child & Adolescent Psychiatry: pp. 794-796.

Shadish, William R., et al. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference, Houghton Mifflin Company, Boston: 103-243.

Threats to Validity in an Experimental Design

threats to validity in an experimental design. Your response should include an evaluation of the choice of design, the author's rationale for the design choice, the types of validity presented and the critical differences among them, the author's performance in explaining them, and how you would assess the study's validity and the information you would require to do so. Choice of research design: The efficacy of female condom skills training in HIV risk reduction among women andomized clinical trials are often considered the 'gold standard' of good medical research. This is because randomized trials make use of an experimental and control group and the randomization process is designed to eliminate possible selection bias, which causes correlative rather than correlative factors to potentially skew results. In the case of Choi (et al. 2008) according to the study "The efficacy of female condom skills training in HIV risk reduction among women" a "randomized trial….

Choi, K. (et al. 2008).The efficacy of female condom skills training in HIV risk reduction among women: A randomized controlled trial. American Journal of Public

Health, 98 (10):1841-1848

Experimental Research Methods in Business Experimental Research

Experimental esearch Methods in Business Experimental esearch Methods The author provides a survey of the literature illustrating applied experimental research methods in cross-sections of business and organization types. The advantages and disadvantages of the experimental research methods are discussed for each of the examples provided which run the gamut from depression-era agricultural economics to research conducted for the National Science Institute. While the article focuses on business research methods, the range of examples from multiple disciplines serves to demonstrate the adaptability of various methods to distinct contexts, the importance of thoughtfully developed research questions, and perceptions in the field regarding scientific rigor. The article is intended to guide students in their exploration of the breadth and depth of experimental research methods and to convey a sense of the challenges of applied scientific inquiry. Introduction The study of business topics has not always been inherently scientific. Certainly the work of Max Weber and Frederick Winslow….

Campbell, A. (2004). A quick guide to research methods, Australian and New Zealand Journal of Family Therapy, 25(3), 163-165.

Cooper, D.R. And Schindler, P.S. (2011). Business research methods. New York, NY: McGraw Hill.

Demarco, T., Hruschka, P., Lister, T., Robertson, S., Robertson, J., and McMenamin, S. (2008). Adrenaline junkies and template zombies: Understanding patterns of project behavior. New York, NY: Dorset House Publishing Co., Inc.

Elliott F.F. (1929, October). Experimental method in economic research, Journal of Farm Economics, 11 (4) 594-596. [Oxford University Press on behalf of the Agricultural & Applied Economics Association]. Retrieved http://www.jstor.org/stable/1229899

Experimental Research Design the Research

e., contemporary or historical issues (Eisenhardt 1989; in Naslund, 2005); (3) the extent of control required over behavioral events in the research context (Yin 1994; as cited in Naslund, 2005); and (4) the researcher's philosophical stance, i.e., his/her understanding of the nature of social reality and how knowledge of that reality can be gained. (Naslund, 2005) Naslund (2005) states that qualitative research methods "primarily create meanings and explanations to research phenomena" and include data collection methods such as: (1) Observation; (2) Fieldwork including interviews and questionnaires, diary methods, documents and texts, case studies; and (3) the researcher's impressions and reactions to observed phenomena. Quantitative research methods serve to make provision of a broad range of situations as well as being fast and economical. Commonly utilized quantitative research methods include those of: (1) Laboratory experiments; (2) Formal methods; and (3) Numerical methods and techniques. (Naslund, 2005) Naslund states that analysis identifies a number of interesting trends and trends that….

BIBLIOGRAPHY

Experimental Research (2009) Experimental Resources. Online available at:  

Experimental Research an Experiment Is a Form

Experimental Research An experiment is a form of quantitative research that tests causal relationships. The researcher manipulates and controls the conditions under which individuals are observed to behave. Experimental research starts with a hypothesis and then modifies something in a particular relationship. The researcher has control over the environment, variables and individuals under study. At the end of the experiment, the outcome is compared with the situation before the modification. An experiment consists of a number of components: Treatment or independent variable Dependent variable Pre-test Post-test Experimental group Control group Random assignment Classical Experimental, Pre-Experimental, Quasi-Experimental and the Solomon Four-Group designs all differ in how they treat these components, thus impacting the reliability and validity of the experiment. Classical Experimental Design comprises random assignment of cases to groups, a pre-test and a post-test, an experimental group and a control group. Each group is exposed to different conditions or stimulus materials. Random assignment is used to increase the likelihood that each….

Describe the 2 theoretical perspectives behind research. Develop a research question. Justify the theoretical perspectives chosen to answer your research question. Critically review appropriate literature literature.

1. The two theoretical perspectives behind research are the positivist perspective and the interpretivist perspective. - Positivist perspective: This perspective focuses on the idea that knowledge can be gained through objective observation and measurement. Positivists believe that there is an objective reality that can be studied and understood through empirical evidence and scientific methods. - Interpretivist perspective: This perspective emphasizes the importance of understanding the subjective meanings and interpretations that individuals attach to their experiences. Interpretivists believe that reality is socially constructed and that individuals' interpretations of the world are shaped by their unique perspectives, beliefs, and values. 2. Research question: How....

Need assistance developing essay topics related to Developmental Psychology. Can you offer any guidance?

Developmental Psychology: A Comprehensive List of Essay Topics Cognitive Development Piaget's Theory of Cognitive Development: An Examination of its Stages and Implications Information Processing in Children: How Age and Experience Shape Cognitive Function Language Development and the Role of Environment: Exploring the Interplay of Nature and Nurture Cognitive Biases in Children and Adolescents: The Impact of Cognitive Immaturity on Decision-Making The Development of Memory in Infancy: How Early Experiences Influence Retrieval and Recognition Social and Emotional Development Attachment Theory: Bowlby's and Ainsworth's Perspectives: Implications for Infant-Caregiver Relationships Socialization and the Development of Prosocial Behavior: How Children Learn to Cooperate and Share Moral....

Research Paper

Experimental design employs comparison as its strategy for the given research. It uses two groups, which the researcher uses for comparison purposes. These include the experimental group and a…

Methodology Chapter

Dependent Variable 1: Strength of Expressed Opinions on Questionnaire. Independent Variable 2: Exposure to Indirect Intervention. Dependent Variable 2: Strength of Expressed Opinions on Questionnaire. Variables Direct Intervention -- Test subjects will be…

Agriculture

Experimental Design for Hypothetial Researh Study Reent researh has emerged whih suggests that the ingestion of hoolate may lead to improved ognitive funtion within the realm of memorization and retention…

Education - Administration

Experimentation is one of the common methods used in quantitative research. Premised on the positivist philosophy, an experiment is essentially conducted to investigate causal relationships between variables (Bryman, 2008).…

Article Review

Criminal Justice

experimental design feasible? Why or why not? • What suggestions can you make for future studies of the DARE program? The aims of DARE are long-term in nature, namely to…

Research Proposal

Transportation

goal of the research is to conduct an assessment of probable issues relating to hiring a deputy director for the organization, which has operated without an actual office…

featuring a QUANTITATIVE experimental design related criminal justice security management. Attach article ( a hyperlink article) posting. Please answer questions: Overview: Provide an overview study ( -write abstract;…

Ball-And-Sock Experiment he Experimental Design I altered the experiment slightly to help make sure that it would demonstrate the issues it was supposed to demonstrate without adding extraneous variables. Specifically,…

Business - Miscellaneous

limiting a researcher's view of the problem are situational factors that can skew the results of her experiment, i.e., effects of pretesting, social threats, and group differences (Trochim,…

Threats to Validity in a Quasi-Experimental Design Evaluating design choice: Walk Texas! The research study by Bartholomew (et al. 2008) entitled "Walk Texas! 5-A-Day intervention for women, infant, and children…

Sports - College

Quasi-Experimental Design on the Effect of TV Adverts on Children This study carries out the evaluation of a research titled "A quasi-experiment assessing the effectiveness of TV advertising directed to…

Women's Issues - Sexuality

threats to validity in an experimental design. Your response should include an evaluation of the choice of design, the author's rationale for the design choice, the types of…

Literature Review

Experimental esearch Methods in Business Experimental esearch Methods The author provides a survey of the literature illustrating applied experimental research methods in cross-sections of business and organization types. The advantages and…

Education - Mathematics

e., contemporary or historical issues (Eisenhardt 1989; in Naslund, 2005); (3) the extent of control required over behavioral events in the research context (Yin 1994; as cited in Naslund, 2005);…

Transportation - Environmental Issues

Experimental Research An experiment is a form of quantitative research that tests causal relationships. The researcher manipulates and controls the conditions under which individuals are observed to behave. Experimental research…

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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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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Rules of Experimental Designs Informative Essay

An experimental design is a control mechanism accomplished by using a controlled variable. Experimental designs are carried out randomly among a group of subjects chosen for the purpose of the study (Hergenhahn, 2005).

In experiments, there should be two groups of subjects namely, the experimental group within which the scientist controls the variables, and a control group within which the conditions are left to be as they were before the start of the experiment (Patterson, 1996).

Quasi experimental designs are those which occur naturally. The designs seek to find out the impact of a change on a specific aspect of a population. Quasi designs do not have the random quality unlike experimental designs. Ten tomato plants were planted in different containers.

They were subjected to the same environmental conditions in which the lighting was the same for all the plants (Gazzaniga, 2010). The only aspect which was altered was the amount of water which was used on the plants. The results were recorded every day for a period of 30 days (Seligman& Reichenberg, 2009).

In a second experiment which was performed to test the quasi experimental design, 10 tomato plants were selected from two different farms located in two locations of differing climate. Every day, the height of the tomato plants was measured for a period of 30 days.

Water supports life in both animals and plants because the aqueous aspect is essential for the functional shape of many of the important cellular molecules including the proteins and lipids which are the building blocks of living matter (Dunne, 1978). Growth takes place when the cells absorb water in the process of osmosis.

The intracellular force enlarges the cellulosic membrane that holds the cells (Holbrook, 2010). Water is usually a necessary raw material in the process of photosynthesis. A plant’s chloroplast forms the basis for conversion of sunlight energy into carbon dioxide and molecules of water.

Consequently, water and carbon dioxide are converted into carbohydrate and oxygen (Dunne, 1978). Plants which lack water usually wilt and eventually die.

Clay soil can retain water for a longer time than other types of soils; hence plants growing on clay soil would not need to be watered regularly than those growing on sandy soil. Recently germinated plants may require more water as their roots may not have developed fully.

Older plants would however require minimum supply of water. The materials required include 10 tomato seedlings, 10 plastic containers, soil, water, 1 measuring cylinder, ruler which is 1 centimeter long and 1 marker.

The independent variable is the amount of water used every day with capacities ranging from 25ml, 45ml, 65ml, 85ml and 105ml once or twice a day. The height of the potato stems defines the dependent variable.

The control variables are equated to the constant which becomes the environment. Adequate temperature and humidity should be supplied to all the plants. The type of soil and size of container used constitute other constants. All the containers were filled with loam soil.

Tomato seeds were then planted in every container. All the pots were labeled accordingly for easy identification according to the various capacities between 25 milliliters to 105milliliters. The containers labeled “once”, were filled with water only once a day according to the given capacities of the containers.

The ones labeled “twice”, were filled with water two times in a day according to the specified amounts of the containers. Every ten days, the tomato plants were measured to note their heights over the ten day period.

An average height was calculated. Results showed that plants that were given 45 milliliters of water twice in a day grew faster in height than the ones which were given water of smaller capacity after observations were made on the tenth day.

The above results support the hypothesis that the greater the amount of water given to tomato plants, the faster their rate of growth (Covey, 2004).The water gets into the cellular organelles and the cells and they start to increase in size in a process called plasmolysis .

Increase in the size of the cells results to increase in the height of the plant hence growth. This fact only applies to an optimum point beyond which any additional water that is added results in a decline in the height of the tomato plants. Two separate groups of families which had a history of domestic violence were studied.

The two groups were first tested to confirm the incidences of domestic violence in their homes before the experiment was started. In the first group, subjects were provided with training on domestic violence. Subjects in the second group were however not subjected to any form of training.

At the end of the experiment, the subjects were tested to confirm the number of incidences of domestic violence during a period of one year. After the study, about 840,000 women reported that domestic violence was executed on them by their husbands.

Specific interventions were undertaken to address the issue of domestic violence. These interventions were directed on males with the sole objective of deterring them from further violence on their spouses.

According to Patterson, very little or no intervention was undertaken on domestic violence perpetrators (Patterson, 1996).

This fact was mainly due to the issues of privacy as compared to the best interest of the society. In 1874, domestic violence was considered an issue to be dealt with within the respective homes in which it occurred (State versus Oliver, 1874, cited in Rosenfeld, 1992).

In later years, however domestic violence has been classified as a criminal offence. Abusers were subjected to either incarceration or rehabilitation depending on the nature of the offense.

A quasi experimental design is a type of study which shows the impact of changing a certain aspect, which in turn affects the subjects when such an experiment lacks the characteristic of randomness. To check for the effect of the intervention, there is normally a pre and post-test.

Tests are made prior to data collection to assess whether there are confounding elements. The pretest data can be included in an explanation of the actual experimental data (Morgan, 2002).

Unlike in the experimental design, in a quasi-experimental design, it is not possible to choose which variables will be in the control group and which ones will be in another group.

An example of a quasi-experimental design is where a researcher aims at finding out the effect of rewarding farmers for planting trees on their farms. The control will be another farm where farmers are not rewarded. The number of trees in each farm is counted before and after the intervention.

The independent variable is the training on domestic violence. The independent variable is the cause of the effect on the dependent variable. The dependent variables are the incidences of domestic violence. The hypothesis states that the training on domestic violence reduces incidences of domestic violence.

The particular training causes the individual to control his or her behavior.This fact will have an effect on the number of incidences of domestic violence. Before the beginning of this quasi experiment design, subjects are first evaluated by the number of past incidences of domestic violence.

They are then exposed to training on domestic violence. At the end of the experiment, subjects are tested again by recording the number of domestic violence incidents. This study poses some validity challenges. The testing before the experiment may influence the results of the test at the end of the experiment.

Two separate groups are studied. In the first group 10 families are selected. In this group, training on domestic violence is administered. However, a second group of 10 families are not subjected to any kind of training at all. Both groups are made up of domestic violence offenders.

In this particular case, a quasi-experimental design is required because it is not possible to subject each of the families to the same conditions. The data and findings of this report indicate that the subjects had a history of assaults and were also involved in other criminal activities.

They also had addiction problems and substance abuse. Counselors pointed out that at times, treatment had to be suspended so that the subjects could be cured of their addictions.

Despite the fact that perpetrators of violence at the domestic level were very difficult to deal with, most of them showed significant improvement when subjected to training on domestic violence. Within a period of twelve months after the experiment, 10% of them had assaulted again.

This percentage was very low compared to the 31% rate identified by Gazzaniga, (2010) and 32% observed by Patterson, (1996).

While this program appears to reduce the rate of domestic violence, it is not possible to distinguish whether it is the training alone which had this effect or because the subjects were cured of substance addiction (Palmer &Woolf, 1999). This idea shows that this method of research which is quasi experimental is not very reliable.

This method of research has its limitations. Its major limitation is the fact that it does not meet the criteria of random assignment. It is also not possible to infer the cause and effect. However, in a quasi-experiment, individuals do not need to be grouped as they are naturally pre-grouped (Morgan, 2002).

Secondly, it is advantageous in that two groups can be compared to provide accurate results. A quasi experiment has its disadvantages. The risk of two groups not being identical exists. This fact has the effect of distorting the results (Murphy, & Dillon, 2011).

A second disadvantage is due to the fact that it is not possible to tell whether there are other factors which influence the outcome. In conclusion, I would prefer to use an experimental design because of the random selection of subjects.

A quasi experimental design gives the researcher a varied scope of analysis and the likelihood of making an unexpected finding. This design provides uniformity and accuracy of results. However, in certain selected cases, using a quasi-experimental design is the only viable option.

Covey, S. R. (2004). The 7 Habits of Highly Effective People . New York, USA: Free Press A Division of Simon and Schuster, Inc.

Dunne, Thomas and Leopold, Luna, Water in Environmental Planning . W. H. Freeman, San Francisco, CA; c1978.

Gazzaniga, M. (2010). Psychological Science . New York, USA: W.W. Norton & Company.

Hergenhahn, B.R. (2005). An introduction to the history of psychology . Belmont, USA: Thomson Wadsworth.

Holbrook, N. Michelle. Water balance of plants in plant physiology (Lincoln Taiz and Eduardo Zeiger editors). Sunderland, United Kingdom: Sinauer Associates.

Morgan G. A (2002) Journal of the American Academy of Psychiatry: Quasi Experimental Design. Connecticut, USA: Cengage Learning.

Murphy, B. C., & Dillon, C. (2011). Interviewing in Action in a Multicultural world (4 th ed.). Belmont, CA: Brooks/Cole.

Palmer, S. M., &Woolf, R. (1999). Integrative and eclectic counseling and psychotherapy. London, UK: Sage Publications.

Patterson, C. H. (1996). Multicultural counseling: From diversity to universality. Journal of Counseling and Development, JCD, 74(3), 227.

Seligman, L. W., & Reichenberg, L. W. (2009). Theories of counseling and psychotherapy: Systems, strategies, and skills (3rd ed.) . Boston, USA: Pearson.

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1.3: Experimental Design

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• Page ID 130229

• Kathryn Kozak
• Coconino Community College

The section is an introduction to experimental design. This is how to actually design an experiment or a survey so that they are statistical sound. Experimental design is a very involved process, so this is just a small introduction.

Guidelines for planning a statistical study

• . Identify the individuals that you are interested in. Realize that you can only make conclusions for these individuals. As an example, if you use a fertilizer on a certain genus of plant, you can’t say how the fertilizer will work on any other types of plants. However, if you diversify too much, then you may not be able to tell if there really is an improvement since you have too many factors to consider.
• Specify the variable. You want to make sure this is something that you can measure, and make sure that you control for all other factors too. As an example, if you are trying to determine if a fertilizer works by measuring the height of the plants on a particular day, you need to make sure you can control how much fertilizer you put on the plants (which would be your treatment), and make sure that all the plants receive the same amount of sunlight, water, and temperature.
• Specify the population. This is important in order for you know what conclusions you can make and what individuals you are making the conclusions about.
• Specify the method for taking measurements or making observations.
• Determine if you are taking a census or sample. If taking a sample, decide on the sampling method.
• Collect the data.
• Use appropriate descriptive statistics methods and make decisions using appropriate inferential statistics methods.
• Note any concerns you might have about your data collection methods and list any recommendations for future.

There are two types of studies:

Definition \(\PageIndex{1}\)

An observational study is when the investigator collects data merely by watching or asking questions. He doesn’t change anything.

Definition \(\PageIndex{2}\)

An experiment is when the investigator changes a variable or imposes a treatment to determine its effect.

Example \(\PageIndex{1}\) observational study or experiment

State if the following is an observational study or an experiment.

• Poll students to see if they favor increasing tuition.
• Give some students a tutor to see if grades improve.
• This is an observational study. You are only asking a question.
• This is an experiment. The tutor is the treatment.

Many observational studies involve surveys. A survey uses questions to collect the data and needs to be written so that there is no bias.

In an experiment, there are different options.

Randomized Two-Treatment Experiment:

In this experiment, there are two treatments, and individuals are randomly placed into the two groups. Either both groups get a treatment, or one group gets a treatment and the other gets either nothing or a placebo. The group getting either no treatment or the placebo is called the control group. The group getting the treatment is called the treatment group. The idea of the placebo is that a person thinks they are receiving a treatment, but in reality they are receiving a sugar pill or fake treatment. Doing this helps to account for the placebo effect, which is where a person’s mind makes their body respond to a treatment because they think they are taking the treatment when they are not really taking the treatment. Note, not every experiment needs a placebo, such when using animals or plants. Also, you can’t always use a placebo or no treatment. As an example, if you are testing a new blood pressure medication you can’t give a person with high blood pressure a placebo or no treatment because of moral reasons.

Randomized Block Design:

A block is a group of subjects that are similar, but the blocks differ from each other. Then randomly assign treatments to subjects inside each block. An example would be separating students into full-time versus part-time, and then randomly picking a certain number full-time students to get the treatment and a certain number part-time students to get the treatment. This way some of each type of student gets the treatment and some do not.

Rigorously Controlled Design:

Carefully assign subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment. An example would be if you want to have a full-time student who is male, takes only night classes, has a full-time job, and has children in one treatment group, then you need to have the same type of student getting the other treatment. This type of design is hard to implement since you don’t know how many differentiations you would use, and should be avoided.

Matched Pairs Design :

The treatments are given to two groups that can be matched up with each other in some ways. One example would be to measure the effectiveness of a muscle relaxer cream on the right arm and the left arm of individuals, and then for each individual you can match up their right arm measurement with their left arm. Another example of this would be before and after experiments, such as weight before and weight after a diet.

No matter which experiment type you conduct, you should also consider the following:

Replication :

Repetition of an experiment on more than one subject so you can make sure that the sample is large enough to distinguish true effects from random effects. It is also the ability for someone else to duplicate the results of the experiment.

Blind Study :

Blind study is where the individual does not know which treatment they are getting or if they are getting the treatment or a placebo.

Double-Blind Study:

Double-blind study is where neither the individual nor the researcher knows who is getting which treatment or who is getting the treatment and who is getting the placebo. This is important so that there can be no bias created by either the individual or the researcher.

One last consideration is the time period that you are collecting the data over. There are three types of time periods that you can consider.

Cross-Sectional Study:

Data observed, measured, or collected at one point in time.

Retrospective (or Case-Control) Study:

Data collected from the past using records, interviews, and other similar artifacts.

Prospective (or Longitudinal or Cohort) Study:

Data collected in the future from groups sharing common factors.

Exercise \(\PageIndex{1}\)

• You want to determine if cinnamon reduces a person’s insulin sensitivity. You give patients who are insulin sensitive a certain amount of cinnamon and then measure their glucose levels. Is this an observation or an experiment? Why?
• You want to determine if eating more fruits reduces a person’s chance of developing cancer. You watch people over the years and ask them to tell you how many servings of fruit they eat each day. You then record who develops cancer. Is this an observation or an experiment? Why?
• A researcher wants to evaluate whether countries with lower fertility rates have a higher life expectancy. They collect the fertility rates and the life expectancies of countries around the world. Is this an observation or an experiment? Why?
• To evaluate whether a new fertilizer improves plant growth more than the old fertilizer, the fertilizer developer gives some plants the new fertilizer and others the old fertilizer. Is this an observation or an experiment? Why?
• A researcher designs an experiment to determine if a new drug lowers the blood pressure of patients with high blood pressure. The patients are randomly selected to be in the study and they randomly pick which group to be in. Is this a randomized experiment? Why or why not?
• Doctors trying to see if a new stint works longer for kidney patients, asks patients if they are willing to have one of two different stints put in. During the procedure the doctor decides which stent to put in based on which one is on hand at the time. Is this a randomized experiment? Why or why not?
• A researcher wants to determine if diet and exercise together helps people lose weight over just exercising. The researcher solicits volunteers to be part of the study, randomly picks which volunteers are in the study, and then lets each volunteer decide if they want to be in the diet and exercise group or the exercise only group. Is this a randomized experiment? Why or why not?
• To determine if lack of exercise reduces flexibility in the knee joint, physical therapists ask for volunteers to join their trials. They then randomly select the volunteers to be in the group that exercises and to be in the group that doesn’t exercise. Is this a randomized experiment? Why or why not?
• You collect the weights of tagged fish in a tank. You then put an extra protein fish food in water for the fish and then measure their weight a month later. Are the two samples matched pairs or not? Why or why not?
• A mathematics instructor wants to see if a computer homework system improves the scores of the students in the class. The instructor teaches two different sections of the same course. One section utilizes the computer homework system and the other section completes homework with paper and pencil. Are the two samples matched pairs or not? Why or why not?
• A business manager wants to see if a new procedure improves the processing time for a task. The manager measures the processing time of the employees then trains the employees using the new procedure. Then each employee performs the task again and the processing time is measured again. Are the two samples matched pairs or not? Why or why not?
• The prices of generic items are compared to the prices of the equivalent named brand items. Are the two samples matched pairs or not? Why or why not?
• A doctor gives some of the patients a new drug for treating acne and the rest of the patients receive the old drug. Neither the patient nor the doctor knows who is getting which drug. Is this a blind experiment, double blind experiment, or neither? Why?
• One group is told to exercise and one group is told to not exercise. Is this a blind experiment, double blind experiment, or neither? Why?
• The researchers at a hospital want to see if a new surgery procedure has a better recovery time than the old procedure. The patients are not told which procedure that was used on them, but the surgeons obviously did know. Is this a blind experiment, double blind experiment, or neither? Why?
• To determine if a new medication reduces headache pain, some patients are given the new medication and others are given a placebo. Neither the researchers nor the patients know who is taking the real medication and who is taking the placebo. Is this a blind experiment, double blind experiment, or neither? Why?
• A new study is underway to track the eating and exercise patterns of people at different time periods in the future, and see who is afflicted with cancer later in life. Is this a cross-sectional study, a retrospective study, or a prospective study? Why?
• To determine if a new medication reduces headache pain, some patients are given the new medication and others are given a placebo. The pain levels of a patient are then recorded. Is this a cross-sectional study, a retrospective study, or a prospective study? Why?
• To see if there is a link between smoking and bladder cancer, patients with bladder cancer are asked if they currently smoke or if they smoked in the past. Is this a cross-sectional study, a retrospective study, or a prospective study? Why?
• The Nurses Health Survey was a survey where nurses were asked to record their eating habits over a period of time, and their general health was recorded. Is this a cross-sectional study, a retrospective study, or a prospective study? Why?
• Consider a question that you would like to answer. Describe how you would design your own experiment. Make sure you state the question you would like to answer, then determine if an experiment or an observation is to be done, decide if the question needs one or two samples, if two samples are the samples matched, if this is a randomized experiment, if there is any blinding, and if this is a cross-sectional, retrospective, or prospective study.

1. Experiment

3. Observation

5. No, see solutions

7. No, see solutions

9. Yes, see solutions

11. Yes, see solutions

13. Double blind, see solutions

15. Blind, see solutions

17. Prospective, see solutions

19. Retrospective, see solutions

21. See solutions

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• Experimental Design

Experimental Design - Essay Example

• Subject: Psychology
• Type: Essay
• Level: Undergraduate
• Pages: 2 (500 words)
• Downloads: 21
• Author: gmacejkovic

Extract of sample "Experimental Design"

Hypothesis: Sleep-deprived are more likely to get lower grades on tests. Methodology of experimental design In this study, the proponent will try to approach specific respondents from a University. The researcher should know the total number of the students in the registrar who were officially registered. This is the remarkable basis of having the right number of samples in order to generate statistically significant result. Concerning this study, the proponent will initiate a random sampling so that all probable respondents will have equal chance to be chosen until a desired number of samples is obtained.

At least 10 percent of the population will be randomly chosen and a letter of intent concerning the study will be given to the University and each chosen student. All the respondents will be informed that the study will only take overnight, to check their ability on exams. Some respondents will be randomly immersed to a sponsored party overnight, in a place where they are not allowed to go and only stay there until the end of the study, but no drinks or any alchohol related beverages will be provided.

The participants will also be discouraged to take energy drinks and other related content that could help boost mental performance. Other participants will be placed to an atmosphere where they could have a good night rest, the control group. Prior to the actual study, participants will also be screened concerning their health condition, to make sure that all random samples have better health, ensuring further that the study will reflect on the individuals with good health and their cognitive ability when faced with sleep deprivation.

Students will not be allowed to sleep until a desired time and they also have to wake up on a scheduled time to take the exam with general knowledge questions that would require analysis. Performance on the test should be scaled from poor to best from 1 to 5 based on how they would analyze a very objective question. Dependent and independent variablesThe dependent variables of this research study is the students’ actual performance or score on the test. The independent variables are the hour of sleep incurred prior to taking the exam and the students’ actual record of academic performance.

This study includes the actual generated way of creating sleep deprivation in order to test whether this could affect the students’ performance on the test. However, one potential variable that could also influence the students’ performance is their actual academic performance. This could potentially reflect their innate mental capacity. In this study, if there is no significant impact of their mental performance on their regular daily activities to their actual test performance, then the proponent could remarkably justify that sleep deprivation indeed, provided that there would be significant result could be a potential independent variable to affect the students’ grades on tests.

Experimental and control conditionsThe experimental and control conditions include the point of making sure that the entire study would produce relevant result. That is why to justify the experiment, the respondents will initiate another set of exams to be employed to control groups, those who received enough hours of sleep. Thus, equal number of respondents from the experimental and control group should be remarkable. Furthermore, the record of actual mental performance of the chosen respondents will be taken into account.

That is, their actual academic performance in school should be generated in order to create a good idea of the actual condition of the experiment.

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Experimental Design College Essays Samples For Students

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Research Design Matrix And Experimental And Survey Research Methods Essay Example

1.0 Abstract Industrial and organization research is normally concerned with human mental processes and behavior in organizations and businesses. Organizational research normally uses the quantitative research method because most the problems that are studied meet the four objectives relevant to scientific understanding; description, explanation, control, and prediction. However, qualitative research is increasingly becoming relevant in Industrial and organization research. This creates the need to understand both qualitative and quantitative research techniques.

Free Essay On Comparison Of True And Quasi Experimental Designs

Introduction.

Experiments are designed to test the hypothesis. In an experimental design or design of experiment (DOE), participants are assigned to variable conditions in an experiment. Usually, experiments are designed in such a way that participants are divided into two groups, i.e. a control group and an experimental group, and then a change is introduced in the experimental group to determine the outcome. There are two important types of experimental design; True experiments and Quasi-experiments. Both types of experimental designs are used to check the cause of some particular phenomenon.

Types of each design

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Applications Of Research Methods Essay To Use For Practical Writing Help

The comparison of the attentiveness and performance of teenage boys and girls in school.

Quasi-experimental design and the topic The Quasi-Experimental research design is that method that can be used in the study of groups in the comparative approach. This methodology is tested on the basis of how it can be used to achieve the provided goals. It also limits the biasness that can be associated with the research. What makes this methodology to be better suited to the topic is that it is used to test the causal hypothesis (White & Sabarwal, 2014). It can therefore provide a better way of studying and understanding the topic.

Research Question

Good essay on research design, independent variable and dependent variable, research project (x): example essay by an expert writer to follow, application for review of research involving human subjects.

For IRB approval, submit your proposal to: Dr. State University IRB, if you have questions or wish to check the status of your proposal, please call Dr. ALL APPLICATIONS MUST BE TYPED. Please fill in this application form completely. [Do not state, "refer to pages in proposal" for requested information.] Attach additional information to this form only after the space available for response to a given question has been used.

Free Experimentation Essay: Top-Quality Sample To Follow

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Where does this research fall under based on the intent of the researcher? Explain in depth The study falls under the category of evaluation research. Evaluation research focuses on the processes and outcomes of attempted solutions. The current study is a pilot test aimed at evaluating the effectiveness of an asthma intervention program for school children “Okay with Asthma”. The research study was specifically aimed at evaluating the effectiveness of the program in improving the asthma knowledge and attitudes of children towards their own illness.

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In which paragraph(s) of the study is a research problem stated?

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