Example: Factorial design applied in optimisation technique.
To meet the ethical considerations, you need to ensure that.
Collect the data by using suitable data collection according to your experiment’s requirement, such as observations, case studies , surveys , interviews , questionnaires, etc. Analyse the obtained information.
Write the report of your research. Present, conclude, and explain the outcomes of your study .
What is the first step in conducting an experimental research.
The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.
What are the different types of research you can use in your dissertation? Here are some guidelines to help you choose a research strategy that would make your research more credible.
You might have come across the word: range a lot. Not just in statistics but almost in every subject. Ever wondered what does it mean? This article will answer all your questions on range, its calculation and uses.
This article provides the key advantages of primary research over secondary research so you can make an informed decision.
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Neag School of Education
Experimental research.
The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable. There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.
The statistics by themselves have no meaning. They only take on meaning within the design of your study. If we just examine stats, bread can be deadly . The term validity is used three ways in research…
There are several common threats to internal validity in experimental research. They are described in our text. I have review each below (this material is also included in the PowerPoint Presentation on Experimental Research for this unit):
When planning a study, it is important to consider the threats to interval validity as we finalize the study design. After we complete our study, we should reconsider each of the threats to internal validity as we review our data and draw conclusions.
Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com
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.
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!
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.
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.
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:
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.
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!
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.
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?
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.'
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.
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.
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.
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.
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.
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.
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.
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."
What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.
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.
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.
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!
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.
The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.
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.
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.
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.
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 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.
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.
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.
However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.
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.
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.
The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.
But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.
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.
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.
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.
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.
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.
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.
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.
However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.
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.
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.
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.
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.
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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Methodology
Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:
A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.
You might have to write up a research design as a standalone assignment, or it might be part of a larger research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.
Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.
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 approach | Quantitative approach |
---|---|
and describe frequencies, averages, and correlations about relationships between variables |
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.
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 .
At each stage of the research design process, make sure that your choices are practically feasible.
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.
Quantitative designs can be split into four main types.
Type of design | Purpose and characteristics |
---|---|
Experimental | relationships effect on a |
Quasi-experimental | ) |
Correlational | |
Descriptive |
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.
Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.
The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.
Type of design | Purpose and characteristics |
---|---|
Grounded theory | |
Phenomenology |
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.
A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.
For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?
The more precisely you define your population, the easier it will be to gather a representative sample.
Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.
To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.
Probability sampling | Non-probability sampling |
---|---|
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.
In some types of qualitative designs, sampling may not be relevant.
For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.
In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .
For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.
Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.
You can choose just one data collection method, or use several methods in the same study.
Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .
Questionnaires | Interviews |
---|---|
) |
Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.
Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.
Quantitative observation | |
---|---|
There are many other ways you might collect data depending on your field and topic.
Field | Examples of data collection methods |
---|---|
Media & communication | Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives |
Psychology | Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time |
Education | Using tests or assignments to collect data on knowledge and skills |
Physical sciences | Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition |
If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.
If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.
With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.
Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.
However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.
As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.
Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.
Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.
If you’re using observations , which events or actions will you count?
If you’re using surveys , which questions will you ask and what range of responses will be offered?
You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.
Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.
Reliability | Validity |
---|---|
) ) |
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.
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:
If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?
If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?
It’s also important to create a data management plan for organizing and storing your data.
Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.
Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).
On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.
In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.
Using descriptive statistics , you can summarize your sample data in terms of:
The specific calculations you can do depend on the level of measurement of your variables.
Using inferential statistics , you can:
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.
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 .
Approach | Characteristics |
---|---|
Thematic analysis | |
Discourse analysis |
There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.
If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
Statistics
Research bias
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.
Quantitative research designs can be divided into two main categories:
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.
The priorities of a research design can vary depending on the field, but you usually have to specify:
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.
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Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.
It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.
Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.
There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.
Theoretical research.
Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.
Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.
Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.
This type of research is subdivided into two types:
Exploratory research.
Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.
Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.
The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.
In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.
Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.
The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.
Qualitative research.
Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.
In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).
Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.
Experimental research.
It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.
Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.
It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.
Deductive investigation.
In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.
In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.
It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.
Longitudinal study (also referred to as diachronic research).
It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .
Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.
Primary research.
This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.
Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.
Documentary (cabinet).
Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.
Field research study involves the direct collection of information at the location where the observed phenomenon occurs.
Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.
Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.
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The scope of the study is defined at the start of the study. It is used by researchers to set the boundaries and limitations within which the research study will be performed.
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The unit of analysis refers to the main parameter that you’re investigating in your research project or study.
The term monotonic relationship is a statistical definition that is used to describe the link between two variables.
Dr Joseph gained her PhD in Chemistry from the University of Cambridge in 2018 and is now a Postdoctoral Research Associate in Physics at the University. Her research is on better understanding how cells organises its contents via the process of liquid-liquid phase separation.
Maria is a 1st year PhD student at the University of Birmingham, researching how to employ and exploit the biology of human gamma delta T cells for development of cancer immunotherapy.
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Defne Çobanoğlu
Life and its secrets can only be proven right or wrong with experimentation. You can speculate and theorize all you wish, but as William Blake once said, “ The true method of knowledge is experiment. ”
It may be a long process and time-consuming, but it is rewarding like no other. And there are multiple ways and methods of experimentation that can help shed light on matters. In this article, we explained the definition, types of experimental research, and some experimental research examples . Let us get started with the definition!
Experimental research is the process of carrying out a study conducted with a scientific approach using two or more variables. In other words, it is when you gather two or more variables and compare and test them in controlled environments.
With experimental research, researchers can also collect detailed information about the participants by doing pre-tests and post-tests to learn even more information about the process. With the result of this type of study, the researcher can make conscious decisions.
The more control the researcher has over the internal and extraneous variables, the better it is for the results. There may be different circumstances when a balanced experiment is not possible to conduct. That is why are are different research designs to accommodate the needs of researchers.
There is more than one dividing point in experimental research designs that differentiates them from one another. These differences are about whether or not there are pre-tests or post-tests done and how the participants are divided into groups. These differences decide which experimental research design is used.
Types of experimental research designs
This is the most basic method of experimental study. The researcher doing pre-experimental research evaluates a group of dependent variables after changing the independent variables . The results of this scientific method are not satisfactory, and future studies are planned accordingly. The pre-experimental research can be divided into three types:
Only one variable is considered in this one-shot case study design. This research method is conducted in the post-test part of a study, and the aim is to observe the changes in the effect of the independent variable.
In this type of research, a single group is given a pre-test before a study is conducted and a post-test after the study is conducted. The aim of this one-group pre-test post-test research design is to combine and compare the data collected during these tests.
In a static group comparison, 2 or more groups are included in a study where only a group of participants is subjected to a new treatment and the other group of participants is held static. After the study is done, both groups do a post-test evaluation, and the changes are seen as results.
This research type is quite similar to the experimental design; however, it changes in a few aspects. Quasi-experimental research is done when experimentation is needed for accurate data, but it is not possible to do one because of some limitations. Because you can not deliberately deprive someone of medical treatment or give someone harm, some experiments are ethically impossible. In this experimentation method, the researcher can only manipulate some variables. There are three types of quasi-experimental design:
A nonequivalent group design is used when participants can not be divided equally and randomly for ethical reasons. Because of this, different variables will be more than one, unlike true experimental research.
In this type of research design, the researcher does not divide a group into two to make a study, instead, they make use of a natural threshold or pre-existing dividing point. Only participants below or above the threshold get the treatment, and as the divide is minimal, the difference would be minimal as well.
In natural experiments, random or irregular assignment of patients makes up control and study groups. And they exist in natural scenarios. Because of this reason, they do not qualify as true experiments as they are based on observation.
In true experimental research, the variables, groups, and settings should be identical to the textbook definition. Grouping of the participant are divided randomly, and controlled variables are chosen carefully. Every aspect of a true experiment should be carefully designed and acted out. And only the results of a true experiment can really be fully accurate . A true experimental design can be divided into 3 parts:
In this experimental design, the participants are divided into two groups randomly. They are called experimental and control groups. Only the experimental group gets the treatment, while the other one does not. After the experiment and observation, both groups are given a post-test, and a conclusion is drawn from the results.
In this method, the participants are divided into two groups once again. Also, only the experimental group gets the treatment. And this time, they are given both pre-tests and post-tests with multiple research methods. Thanks to these multiple tests, the researchers can make sure the changes in the experimental group are directly related to the treatment.
This is the most comprehensive method of experimentation. The participants are randomly divided into 4 groups. These four groups include all possible permutations by including both control and non-control groups and post-test or pre-test and post-test control groups. This method enhances the quality of the data.
Just as with any other study, experimental research also has its positive and negative sides. It is up to the researchers to be mindful of these facts before starting their studies. Let us see some advantages and disadvantages of experimental research:
When one does experimental research, that experiment can be about anything. As the variables and environments can be controlled by the researcher, it is possible to have experiments about pretty much any subject. It is especially crucial that it gives critical insight into the cause-and-effect relationships of various elements. Now let us see some important examples of experimental research:
When scientists make new medicines or come up with a new type of treatment, they have to test those thoroughly to make sure the results will be unanimous and effective for every individual. In order to make sure of this, they can test the medicine on different people or creatures in different dosages and in different frequencies. They can double-check all the results and have crystal clear results.
The ideal goal of a marketing product, advertisement, or campaign is to attract attention and create positive emotions in the target audience. Marketers can focus on different elements in different campaigns, change the packaging/outline, and have a different approach. Only then can they be sure about the effectiveness of their approaches. Some methods they can work with are A/B testing, online surveys , or focus groups .
Experimental research can be both qualitative and quantitative according to the nature of the study. Experimental research is quantitative when it provides numerical and provable data. The experiment is qualitative when it provides researchers with participants' experiences, attitudes, or the context in which the experiment is conducted.
In true experimental research, the participants are divided into groups randomly and evenly so as to have an equal distinction. However, in quasi-experimental research, the participants can not be divided equally for ethical or practical reasons. They are chosen non-randomly or by using a pre-existing threshold.
The experimentation process can be long and time-consuming but highly rewarding as it provides valuable as well as both qualitative and quantitative data. It is a valuable part of research methods and gives insight into the subjects to let people make conscious decisions.
In this article, we have gathered experimental research definition, experimental research types, examples, and pros & cons to work as a guide for your next study. You can also make a successful experiment using pre-test and post-test methods and analyze the findings. For further information on different research types and for all your research information, do not forget to visit our other articles!
Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.
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Published 16 October, 2023
Research is a process of making observations, forming hypotheses, and testing them. It is important to design the experiment in such a way that it will provide reliable and accurate data. Experimental research design is a process used to test the effects of different treatments. It is important for researchers to have a clear idea of what they are testing and how it will be measured before doing their experiment. This blog post discusses experimental research design, including its importance and steps in the process.
Experimental Research Design is a type of research in which the investigator manipulates one or more independent variables and observes the results. Experimental research designs are concerned with the examination of the effect of the independent variable on the dependent variable, where the independent variable is manipulated through treatment or interventions, & the effect of those interventions is observed on the dependent variable. By facilitating such type of investigation you can have complete control over different types of variables .
In addition , experimental research design enables you to develop an understanding of the relationship between cause and effect. Experimental research is basically a type of research where you can hypothesis testing in research in a scientific manner.
The main objective of experimental research design is to study the relationship between different variables. One biggest advantage of experimental research is that it allows you to have the highest level of control. The level of detail in research and validity of outcome is completely dependent on variations in independent variables.
You can perform such type of research mainly in three situations these are:
The different types of experimental research design are:
It is basically an extent up to which you assign subjects to different groups or situations by the investigator. In experimental research design, you need to follow normal scientific procedures. By applying the pre-experimental design you can easily perform an investigation on a single group. You may need to employ a pre-experimental research design in order to determine the usefulness of further investigation. This type of experiment will collect data before implementing changes, and its purpose is simply to see if there are any noticeable effects on particular groups or individuals at all.
For example, an investigation which you are performing with the intention of identifying the need for implementation of social welfare policy.
It is a type of experimental research where independent variables are manipulated. This type of investigation includes choosing people without performing pre-selection procedures for testing variables. You need to align Quasi-experimental research designs with case studies so that you can easily perform statistical analysis. True experimental design can establish cause-effect relationships within groups better than other types of designs that are not true experiments.
For example, You want to perform an experiment on a pregnant mother in order to analyze the influence of drinking alcohol on embryos.
This is basically one of the correct types of experimental study design. As researcher has applied statistical analysis techniques for supporting or rejecting the hypothesis in research . Experimental research design helps in developing an understanding of the relationship between cause and effect.
For example, if it’s conducted with people who live together as roommates and they’re not randomly assigned to their rooms because they all know each other well enough already.
Designing good experimental research, there are five main steps, as follows:
A research question is your first step in designing an experiment. Once you have found your research question, translate the main variables into experimental hypotheses and control any extraneous or confounding factors that may skew the results of the experiment. You need to define the variables you want to test and make predictions about how they’re related before coming up with a hypothesis for what will happen if one variable changes when another stays constant.
Now that you have a strong conceptual understanding of the system, your hypotheses will be focused and testable. Designing a controlled experiment is essential for research, so here’s how it works. In order to conduct one, you need three things:
You control the independent variable, but that can affect external validity. First, you might need to decide how widely to vary your independent variable, and second, you may need to choose how finely to vary your independent variable.
When conducting an experiment, the size of your sample is important. Larger samples help give more statistical power and provide greater confidence in experimental results. Once that’s figured out, it’s time to assign subjects randomly into treatment groups so each one gets a different level of the treatment. Lastly, there should also be a control group that doesn’t get any treatment
When designing and executing your study, you need to decide how the variable outcomes will be measured. There are many types of measurements that can be used for reliable and valid measurement and minimize the error. For instance, if you wanted to study temperature changes in certain areas over time, one option would be using scientific instruments – and another could be measuring it directly by taking daily readings at different distances from an area where measurable heating or cooling has taken place.
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Experimental research, often considered to be the “gold standard” in research designs, is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.
Experimental research is best suited for explanatory research (rather than for descriptive or exploratory research), where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments , conducted in field settings such as in a real organization, and high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.
Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.
Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favorably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receives a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.
Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the “cause” in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .
Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and assures that each unit in the population has a positive chance of being selected into the sample. Random assignment is however a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group, prior to treatment administration. Random selection is related to sampling, and is therefore, more closely related to the external validity (generalizability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.
Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.
The simplest true experimental designs are two group designs involving one treatment group and one control group, and are ideally suited for testing the effects of a single independent variable that can be manipulated as a treatment. The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs. These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).
Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.
Figure 10.1. Pretest-posttest control group design
The effect E of the experimental treatment in the pretest posttest design is measured as the difference in the posttest and pretest scores between the treatment and control groups:
E = (O 2 – O 1 ) – (O 4 – O 3 )
Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement (especially if the pretest introduces unusual topics or content).
Posttest-only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.
Figure 10.2. Posttest only control group design.
The treatment effect is measured simply as the difference in the posttest scores between the two groups:
E = (O 1 – O 2 )
The appropriate statistical analysis of this design is also a two- group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.
Covariance designs . Sometimes, measures of dependent variables may be influenced by extraneous variables called covariates . Covariates are those variables that are not of central interest to an experimental study, but should nevertheless be controlled in an experimental design in order to eliminate their potential effect on the dependent variable and therefore allow for a more accurate detection of the effects of the independent variables of interest. The experimental designs discussed earlier did not control for such covariates. A covariance design (also called a concomitant variable design) is a special type of pretest posttest control group design where the pretest measure is essentially a measurement of the covariates of interest rather than that of the dependent variables. The design notation is shown in Figure 10.3, where C represents the covariates:
Figure 10.3. Covariance design
Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:
Figure 10.4. 2 x 2 factorial design
Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. R represents random assignment of subjects to treatment groups, X represents the treatment groups themselves (the subscripts of X represents the level of each factor), and O represent observations of the dependent variable. Notice that the 2 x 2 factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the 2 x 3 design will have six treatment groups, and the 2 x 2 x 2 design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a 2 x 2 x 2 factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all, which are called incomplete factorial designs . Such incomplete designs hurt our ability to draw inferences about the incomplete factors.
In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for 3 hours/week of instructional time than for 1.5 hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.
Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomized bocks design, Solomon four-group design, and switched replications design.
Randomized block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full -time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between treatment group (receiving the same treatment) or control group (see Figure 10.5). The purpose of this design is to reduce the “noise” or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.
Figure 10.5. Randomized blocks design.
Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs but not in posttest only designs. The design notation is shown in Figure 10.6.
Figure 10.6. Solomon four-group design
Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organizational contexts where organizational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.
Figure 10.7. Switched replication design.
Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organization is used as the treatment group, while another section of the same class or a different organization in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of a certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impact by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.
Many true experimental designs can be converted to quasi-experimental designs by omitting random assignment. For instance, the quasi-equivalent version of pretest-posttest control group design is called nonequivalent groups design (NEGD), as shown in Figure 10.8, with random assignment R replaced by non-equivalent (non-random) assignment N . Likewise, the quasi -experimental version of switched replication design is called non-equivalent switched replication design (see Figure 10.9).
Figure 10.8. NEGD design.
Figure 10.9. Non-equivalent switched replication design.
In addition, there are quite a few unique non -equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.
Regression-discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to treatment or control group based on a cutoff score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardized test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program. The design notation can be represented as follows, where C represents the cutoff score:
Figure 10.10. RD design.
Because of the use of a cutoff score, it is possible that the observed results may be a function of the cutoff score rather than the treatment, which introduces a new threat to internal validity. However, using the cutoff score also ensures that limited or costly resources are distributed to people who need them the most rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design does not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.
Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.
Figure 10.11. Proxy pretest design.
Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data are not available from the same subjects.
Figure 10.12. Separate pretest-posttest samples design.
Nonequivalent dependent variable (NEDV) design . This is a single-group pre-post quasi-experimental design with two outcome measures, where one measure is theoretically expected to be influenced by the treatment and the other measure is not. For instance, if you are designing a new calculus curriculum for high school students, this curriculum is likely to influence students’ posttest calculus scores but not algebra scores. However, the posttest algebra scores may still vary due to extraneous factors such as history or maturation. Hence, the pre-post algebra scores can be used as a control measure, while that of pre-post calculus can be treated as the treatment measure. The design notation, shown in Figure 10.13, indicates the single group by a single N , followed by pretest O 1 and posttest O 2 for calculus and algebra for the same group of students. This design is weak in internal validity, but its advantage lies in not having to use a separate control group.
An interesting variation of the NEDV design is a pattern matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique, based on the degree of correspondence between theoretical and observed patterns is a powerful way of alleviating internal validity concerns in the original NEDV design.
Figure 10.13. NEDV design.
Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, many experimental research use inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artifact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.
The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if doubt, using tasks that are simpler and familiar for the respondent sample than tasks that are complex or unfamiliar.
In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.
Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…
Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.
Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.
Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.
Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.
Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.
The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:
Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.
The way a researcher assigns subjects to different groups determines the types of experimental research design .
In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:
This design is practical but lacks in certain areas of true experimental criteria.
This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:
True experimental research design should have a variable to manipulate, a control group and random distribution.
With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:
Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:
Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences
Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :
Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.
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Explore Harappa Diaries to learn more about topics such as Main Objective Of Research , Definition Of Qualitative Research , Examples Of Experiential Learning and Collaborative Learning Strategies to upgrade your knowledge and skills.
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Experimental Research: What it is + Types of designs
Experimental Research: Definition, Types and Examples
Experimental Research Designs: Types ...
Experimental Research Designs: Types, Examples & ...
Guide to Experimental Design | Overview, 5 steps ... - Scribbr
What is Experimental Research: Definition, Types & ...
Experimental Design: Definition and Types
Experimental research - Social Science Research
Types of Research Designs Compared | Guide & Examples
Experimental research serves as a fundamental scientific method aimed at unraveling cause-and-effect relationships between variables across various disciplines. This paper delineates the key ...
Content. Experimental research is a cornerstone of scientific inquiry, providing a systematic approach to understanding cause-and-effect relationships and advancing knowledge in various fields. At its core, experimental research involves manipulating variables, observing outcomes, and drawing conclusions based on empirical evidence.
Guide to experimental research design
Experimental research refers to the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to find out the cause-and-effect relationship between two or more variables. The subjects/participants in the experiment are selected and observed. They receive treatments such as changes in room temperature ...
The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable. There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.
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.
What Is a Research Design | Types, Guide & ...
Types of Research - Explained with Examples
What is experimental research: Definition, types & examples
(PDF) Experimental Research Design-types & process
Experimental Research Design is a type of research in which the investigator manipulates one or more independent variables and observes the results. Experimental research designs are concerned with the examination of the effect of the independent variable on the dependent variable, where the independent variable is manipulated through treatment ...
Chapter 10 Experimental Research
Experimental Research Design | Definition, Components & ...
Experimental Research: Meaning And Examples Of ... - Harappa