experimental research examples in marketing

19+ Experimental Design Examples (Methods + Types)

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

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

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

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

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

What Is Experimental Design?

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

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

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

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

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

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

History of Experimental Design

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

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

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

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

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

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

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

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

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

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

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

Key Terms in Experimental Design

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

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

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

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

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

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

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

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

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

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

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

Steps of Experimental Design

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

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

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

Let's get into examples of experimental designs.

1) True Experimental Design

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

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

No sneaky biases here!

True Experimental Design Pros

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

True Experimental Design Cons

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

True Experimental Design Uses

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

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

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

2) Quasi-Experimental Design

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

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

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

Quasi-Experimental Design Pros

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

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

Quasi-Experimental Design Cons

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

Quasi-Experimental Design Uses

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

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

3) Pre-Experimental Design

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

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

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

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

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

Pre-Experimental Design Pros

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

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

Pre-Experimental Design Cons

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

Pre-Experimental Design Uses

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

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

4) Factorial Design

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

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

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

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

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

Factorial Design Pros

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

Factorial Design Cons

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

Factorial Design Uses

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

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

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

5) Longitudinal Design

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

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

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

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

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

Longitudinal Design Pros

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

Longitudinal Design Cons

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

Longitudinal Design Uses

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

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

6) Cross-Sectional Design

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

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

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

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

Cross-Sectional Design Pros

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

Cross-Sectional Design Cons

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

Cross-Sectional Design Uses

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

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

7) Correlational Design

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

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

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

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

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

Correlational Design Pros

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

Correlational Design Cons

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

Correlational Design Uses

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

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

8) Meta-Analysis

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

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

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

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

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

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

Meta-Analysis Pros

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

Meta-Analysis Cons

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

Meta-Analysis Uses

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

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

9) Non-Experimental Design

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

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

Non-Experimental Design Pros

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

Non-Experimental Design Cons

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

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

Non-Experimental Design Uses

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

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

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

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

10) Repeated Measures Design

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

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

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

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

Repeated Measures Design Pros

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

Repeated Measures Design Cons

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

Repeated Measures Design Uses

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

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

11) Crossover Design

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

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

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

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

Crossover Design Pros

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

Crossover Design Cons

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

Crossover Design Uses

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

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

12) Cluster Randomized Design

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

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

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

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

Cluster Randomized Design Pros

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

Cluster Randomized Design Cons

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

Cluster Randomized Design Uses

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

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

13) Mixed-Methods Design

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

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

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

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

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

Mixed-Methods Design Pros

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

Mixed-Methods Design Cons

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

Mixed-Methods Design Uses

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

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

14) Multivariate Design

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

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

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

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

Multivariate Design Pros

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

Multivariate Design Cons

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

Multivariate Design Uses

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

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

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

15) Pretest-Posttest Design

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

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

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

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

Pretest-Posttest Design Pros

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

Pretest-Posttest Design Cons

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

Pretest-Posttest Design Uses

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

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

16) Solomon Four-Group Design

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

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

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

Solomon Four-Group Design Pros

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

Solomon Four-Group Design Cons

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

Solomon Four-Group Design Uses

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

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

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

17) Adaptive Designs

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

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

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

Adaptive Design Pros

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

Adaptive Design Cons

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

Adaptive Design Uses

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

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

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

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

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

18) Bayesian Designs

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

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

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

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

Bayesian Design Pros

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

Bayesian Design Cons

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

Bayesian Design Uses

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

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

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

19) Covariate Adaptive Randomization

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

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

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

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

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

Covariate Adaptive Randomization Pros

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

Covariate Adaptive Randomization Cons

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

Covariate Adaptive Randomization Uses

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

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

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

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

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

20) Stepped Wedge Design

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

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

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

Stepped Wedge Design Pros

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

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

Stepped Wedge Design Cons

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

Stepped Wedge Design Uses

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

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

21) Sequential Design

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

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

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

Sequential Design Pros

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

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

Sequential Design Cons

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

Sequential Design Uses

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

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

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

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

22) Field Experiments

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

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

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

Field Experiment Pros

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

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

Field Experiment Cons

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

Field Experiment Uses

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

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

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

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

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

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

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

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

Related posts:

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• 40+ Famous Psychologists (Images + Biographies)
• 11+ Psychology Experiment Ideas (Goals + Methods)
• The Little Albert Experiment
• 41+ White Collar Job Examples (Salary + Path)

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Experimental Research: Definition, Types, Design, Examples

Appinio Research · 14.05.2024 · 32min read

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. By controlling factors that could influence the outcome, researchers can isolate the effects of specific variables and make reliable inferences about their impact. This guide offers a step-by-step exploration of experimental research, covering key elements such as research design, data collection, analysis, and ethical considerations. Whether you're a novice researcher seeking to understand the basics or an experienced scientist looking to refine your experimental techniques, this guide will equip you with the knowledge and tools needed to conduct rigorous and insightful research.

What is Experimental Research?

Experimental research is a systematic approach to scientific inquiry that aims to investigate cause-and-effect relationships by manipulating independent variables and observing their effects on dependent variables. Experimental research primarily aims to test hypotheses, make predictions, and draw conclusions based on empirical evidence.

By controlling extraneous variables and randomizing participant assignment, researchers can isolate the effects of specific variables and establish causal relationships. Experimental research is characterized by its rigorous methodology, emphasis on objectivity, and reliance on empirical data to support conclusions.

Importance of Experimental Research

• Establishing Cause-and-Effect Relationships : Experimental research allows researchers to establish causal relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. This provides valuable insights into the underlying mechanisms driving phenomena and informs theory development.
• Testing Hypotheses and Making Predictions : Experimental research provides a structured framework for testing hypotheses and predicting the relationship between variables . By systematically manipulating variables and controlling for confounding factors, researchers can empirically test the validity of their hypotheses and refine theoretical models.
• Informing Evidence-Based Practice : Experimental research generates empirical evidence that informs evidence-based practice in various fields, including healthcare, education, and business. Experimental research contributes to improving outcomes and informing decision-making in real-world settings by identifying effective interventions, treatments, and strategies.
• Driving Innovation and Advancement : Experimental research drives innovation and advancement by uncovering new insights, challenging existing assumptions, and pushing the boundaries of knowledge. Through rigorous experimentation and empirical validation, researchers can develop novel solutions to complex problems and contribute to the advancement of science and technology.
• Enhancing Research Rigor and Validity : Experimental research upholds high research rigor and validity standards by employing systematic methods, controlling for confounding variables, and ensuring replicability of findings. By adhering to rigorous methodology and ethical principles, experimental research produces reliable and credible evidence that withstands scrutiny and contributes to the cumulative body of knowledge.

Experimental research plays a pivotal role in advancing scientific understanding, informing evidence-based practice, and driving innovation across various disciplines. By systematically testing hypotheses, establishing causal relationships, and generating empirical evidence, experimental research contributes to the collective pursuit of knowledge and the improvement of society.

Understanding Experimental Design

Experimental design serves as the blueprint for your study, outlining how you'll manipulate variables and control factors to draw valid conclusions.

Experimental Design Components

Experimental design comprises several essential elements:

• Independent Variable (IV) : This is the variable manipulated by the researcher. It's what you change to observe its effect on the dependent variable. For example, in a study testing the impact of different study techniques on exam scores, the independent variable might be the study method (e.g., flashcards, reading, or practice quizzes).
• Dependent Variable (DV) : The dependent variable is what you measure to assess the effect of the independent variable. It's the outcome variable affected by the manipulation of the independent variable. In our study example, the dependent variable would be the exam scores.
• Control Variables : These factors could influence the outcome but are kept constant or controlled to isolate the effect of the independent variable. Controlling variables helps ensure that any observed changes in the dependent variable can be attributed to manipulating the independent variable rather than other factors.
• Experimental Group : This group receives the treatment or intervention being tested. It's exposed to the manipulated independent variable. In contrast, the control group does not receive the treatment and serves as a baseline for comparison.

Types of Experimental Designs

Experimental designs can vary based on the research question, the nature of the variables, and the desired level of control. Here are some common types:

• Between-Subjects Design : In this design, different groups of participants are exposed to varying levels of the independent variable. Each group represents a different experimental condition, and participants are only exposed to one condition. For instance, in a study comparing the effectiveness of two teaching methods, one group of students would use Method A, while another would use Method B.
• Within-Subjects Design : Also known as repeated measures design , this approach involves exposing the same group of participants to all levels of the independent variable. Participants serve as their own controls, and the order of conditions is typically counterbalanced to control for order effects. For example, participants might be tested on their reaction times under different lighting conditions, with the order of conditions randomized to eliminate any research bias .
• Mixed Designs : Mixed designs combine elements of both between-subjects and within-subjects designs. This allows researchers to examine both between-group differences and within-group changes over time. Mixed designs help study complex phenomena that involve multiple variables and temporal dynamics.

Factors Influencing Experimental Design Choices

Several factors influence the selection of an appropriate experimental design:

• Research Question : The nature of your research question will guide your choice of experimental design. Some questions may be better suited to between-subjects designs, while others may require a within-subjects approach.
• Variables : Consider the number and type of variables involved in your study. A factorial design might be appropriate if you're interested in exploring multiple factors simultaneously. Conversely, if you're focused on investigating the effects of a single variable, a simpler design may suffice.
• Practical Considerations : Practical constraints such as time, resources, and access to participants can impact your choice of experimental design. Depending on your study's specific requirements, some designs may be more feasible or cost-effective   than others .
• Ethical Considerations : Ethical concerns, such as the potential risks to participants or the need to minimize harm, should also inform your experimental design choices. Ensure that your design adheres to ethical guidelines and safeguards the rights and well-being of participants.

By carefully considering these factors and selecting an appropriate experimental design, you can ensure that your study is well-designed and capable of yielding meaningful insights.

Experimental Research Elements

When conducting experimental research, understanding the key elements is crucial for designing and executing a robust study. Let's explore each of these elements in detail to ensure your experiment is well-planned and executed effectively.

Independent and Dependent Variables

In experimental research, the independent variable (IV) is the factor that the researcher manipulates or controls, while the dependent variable (DV) is the measured outcome or response. The independent variable is what you change in the experiment to observe its effect on the dependent variable.

For example, in a study investigating the effect of different fertilizers on plant growth, the type of fertilizer used would be the independent variable, while the plant growth (height, number of leaves, etc.) would be the dependent variable.

Control Groups and Experimental Groups

Control groups and experimental groups are essential components of experimental design. The control group serves as a baseline for comparison and does not receive the treatment or intervention being studied. Its purpose is to provide a reference point to assess the effects of the independent variable.

In contrast, the experimental group receives the treatment or intervention and is used to measure the impact of the independent variable. For example, in a drug trial, the control group would receive a placebo, while the experimental group would receive the actual medication.

Randomization and Random Sampling

Randomization is the process of randomly assigning participants to different experimental conditions to minimize biases and ensure that each participant has an equal chance of being assigned to any condition. Randomization helps control for extraneous variables and increases the study's internal validity .

Random sampling, on the other hand, involves selecting a representative sample from the population of interest to generalize the findings to the broader population. Random sampling ensures that each member of the population has an equal chance of being included in the sample, reducing the risk of sampling bias .

Replication and Reliability

Replication involves repeating the experiment to confirm the results and assess the reliability of the findings . It is essential for ensuring the validity of scientific findings and building confidence in the robustness of the results. A study that can be replicated consistently across different settings and by various researchers is considered more reliable. Researchers should strive to design experiments that are easily replicable and transparently report their methods to facilitate replication by others.

Validity: Internal, External, Construct, and Statistical Conclusion Validity

Validity refers to the degree to which an experiment measures what it intends to measure and the extent to which the results can be generalized to other populations or contexts. There are several types of validity that researchers should consider:

• Internal Validity : Internal validity refers to the extent to which the study accurately assesses the causal relationship between variables. Internal validity is threatened by factors such as confounding variables, selection bias, and experimenter effects. Researchers can enhance internal validity through careful experimental design and control procedures.
• External Validity : External validity refers to the extent to which the study's findings can be generalized to other populations or settings. External validity is influenced by factors such as the representativeness of the sample and the ecological validity of the experimental conditions. Researchers should consider the relevance and applicability of their findings to real-world situations.
• Construct Validity : Construct validity refers to the degree to which the study accurately measures the theoretical constructs of interest. Construct validity is concerned with whether the operational definitions of the variables align with the underlying theoretical concepts. Researchers can establish construct validity through careful measurement selection and validation procedures.
• Statistical Conclusion Validity : Statistical conclusion validity refers to the accuracy of the statistical analyses and conclusions drawn from the data. It ensures that the statistical tests used are appropriate for the data and that the conclusions drawn are warranted. Researchers should use robust statistical methods and report effect sizes and confidence intervals to enhance statistical conclusion validity.

By addressing these elements of experimental research and ensuring the validity and reliability of your study, you can conduct research that contributes meaningfully to the advancement of knowledge in your field.

How to Conduct Experimental Research?

Embarking on an experimental research journey involves a series of well-defined phases, each crucial for the success of your study. Let's explore the pre-experimental, experimental, and post-experimental phases to ensure you're equipped to conduct rigorous and insightful research.

Pre-Experimental Phase

The pre-experimental phase lays the foundation for your study, setting the stage for what's to come. Here's what you need to do:

• Formulating Research Questions and Hypotheses : Start by clearly defining your research questions and formulating testable hypotheses. Your research questions should be specific, relevant, and aligned with your research objectives. Hypotheses provide a framework for testing the relationships between variables and making predictions about the outcomes of your study.
• Reviewing Literature and Establishing Theoretical Framework : Dive into existing literature relevant to your research topic and establish a solid theoretical framework. Literature review helps you understand the current state of knowledge, identify research gaps, and build upon existing theories. A well-defined theoretical framework provides a conceptual basis for your study and guides your research design and analysis.

Experimental Phase

The experimental phase is where the magic happens – it's time to put your hypotheses to the test and gather data. Here's what you need to consider:

• Participant Recruitment and Sampling Techniques : Carefully recruit participants for your study using appropriate sampling techniques . The sample should be representative of the population you're studying to ensure the generalizability of your findings. Consider factors such as sample size , demographics , and inclusion criteria when recruiting participants.
• Implementing Experimental Procedures : Once you've recruited participants, it's time to implement your experimental procedures. Clearly outline the experimental protocol, including instructions for participants, procedures for administering treatments or interventions, and measures for controlling extraneous variables. Standardize your procedures to ensure consistency across participants and minimize sources of bias.
• Data Collection and Measurement : Collect data using reliable and valid measurement instruments. Depending on your research questions and variables of interest, data collection methods may include surveys , observations, physiological measurements, or experimental tasks. Ensure that your data collection procedures are ethical, respectful of participants' rights, and designed to minimize errors and biases.

Post-Experimental Phase

In the post-experimental phase, you make sense of your data, draw conclusions, and communicate your findings  to the world . Here's what you need to do:

• Data Analysis Techniques : Analyze your data using appropriate statistical techniques . Choose methods that are aligned with your research design and hypotheses. Standard statistical analyses include descriptive statistics , inferential statistics (e.g., t-tests , ANOVA ), regression analysis , and correlation analysis. Interpret your findings in the context of your research questions and theoretical framework.
• Interpreting Results and Drawing Conclusions : Once you've analyzed your data, interpret the results and draw conclusions. Discuss the implications of your findings, including any theoretical, practical, or real-world implications. Consider alternative explanations and limitations of your study and propose avenues for future research. Be transparent about the strengths and weaknesses of your study to enhance the credibility of your conclusions.
• Reporting Findings : Finally, communicate your findings through research reports, academic papers, or presentations. Follow standard formatting guidelines and adhere to ethical standards for research reporting. Clearly articulate your research objectives, methods, results, and conclusions. Consider your target audience and choose appropriate channels for disseminating your findings to maximize impact and reach.

Chi-Square Calculator :

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By meticulously planning and executing each experimental research phase, you can generate valuable insights, advance knowledge in your field, and contribute to scientific progress.

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Experimental Research Examples

Understanding how experimental research is applied in various contexts can provide valuable insights into its practical significance and effectiveness. Here are some examples illustrating the application of experimental research in different domains:

Market Research

Experimental studies are crucial in market research in testing hypotheses, evaluating marketing strategies, and understanding consumer behavior . For example, a company may conduct an experiment to determine the most effective advertising message for a new product. Participants could be exposed to different versions of an advertisement, each emphasizing different product features or appeals.

By measuring variables such as brand recall, purchase intent, and brand perception, researchers can assess the impact of each advertising message and identify the most persuasive approach.

Software as a Service (SaaS)

In the SaaS industry, experimental research is often used to optimize user interfaces, features, and pricing models to enhance user experience and drive engagement. For instance, a SaaS company may conduct A/B tests to compare two versions of its software interface, each with a different layout or navigation structure.

Researchers can identify design elements that lead to higher user satisfaction and retention by tracking user interactions, conversion rates, and customer feedback . Experimental research also enables SaaS companies to test new product features or pricing strategies before full-scale implementation, minimizing risks and maximizing return on investment.

Business Management

Experimental research is increasingly utilized in business management to inform decision-making, improve organizational processes, and drive innovation. For example, a business may conduct an experiment to evaluate the effectiveness of a new training program on employee productivity. Participants could be randomly assigned to either receive the training or serve as a control group.

By measuring performance metrics such as sales revenue, customer satisfaction, and employee turnover, researchers can assess the training program's impact and determine its return on investment. Experimental research in business management provides empirical evidence to support strategic initiatives and optimize resource allocation.

In healthcare , experimental research is instrumental in testing new treatments, interventions, and healthcare delivery models to improve patient outcomes and quality of care. For instance, a clinical trial may be conducted to evaluate the efficacy of a new drug in treating a specific medical condition. Participants are randomly assigned to either receive the experimental drug or a placebo, and their health outcomes are monitored over time.

By comparing the effectiveness of the treatment and placebo groups, researchers can determine the drug's efficacy, safety profile, and potential side effects. Experimental research in healthcare informs evidence-based practice and drives advancements in medical science and patient care.

These examples illustrate the versatility and applicability of experimental research across diverse domains, demonstrating its value in generating actionable insights, informing decision-making, and driving innovation. Whether in market research or healthcare, experimental research provides a rigorous and systematic approach to testing hypotheses, evaluating interventions, and advancing knowledge.

Experimental Research Challenges

Even with careful planning and execution, experimental research can present various challenges. Understanding these challenges and implementing effective solutions is crucial for ensuring the validity and reliability of your study. Here are some common challenges and strategies for addressing them.

Sample Size and Statistical Power

Challenge : Inadequate sample size can limit your study's generalizability and statistical power, making it difficult to detect meaningful effects. Small sample sizes increase the risk of Type II errors (false negatives) and reduce the reliability of your findings.

Solution : Increase your sample size to improve statistical power and enhance the robustness of your results. Conduct a power analysis before starting your study to determine the minimum sample size required to detect the effects of interest with sufficient power. Consider factors such as effect size, alpha level, and desired power when calculating sample size requirements. Additionally, consider using techniques such as bootstrapping or resampling to augment small sample sizes and improve the stability of your estimates.

To enhance the reliability of your experimental research findings, you can leverage our Sample Size Calculator . By determining the optimal sample size based on your desired margin of error, confidence level, and standard deviation, you can ensure the representativeness of your survey results. Don't let inadequate sample sizes hinder the validity of your study and unlock the power of precise research planning!

Confounding Variables and Bias

Challenge : Confounding variables are extraneous factors that co-vary with the independent variable and can distort the relationship between the independent and dependent variables. Confounding variables threaten the internal validity of your study and can lead to erroneous conclusions.

Solution : Implement control measures to minimize the influence of confounding variables on your results. Random assignment of participants to experimental conditions helps distribute confounding variables evenly across groups, reducing their impact on the dependent variable. Additionally, consider using matching or blocking techniques to ensure that groups are comparable on relevant variables. Conduct sensitivity analyses to assess the robustness of your findings to potential confounders and explore alternative explanations for your results.

Researcher Effects and Experimenter Bias

Challenge : Researcher effects and experimenter bias occur when the experimenter's expectations or actions inadvertently influence the study's outcomes. This bias can manifest through subtle cues, unintentional behaviors, or unconscious biases , leading to invalid conclusions.

Solution : Implement double-blind procedures whenever possible to mitigate researcher effects and experimenter bias. Double-blind designs conceal information about the experimental conditions from both the participants and the experimenters, minimizing the potential for bias. Standardize experimental procedures and instructions to ensure consistency across conditions and minimize experimenter variability. Additionally, consider using objective outcome measures or automated data collection procedures to reduce the influence of experimenter bias on subjective assessments.

External Validity and Generalizability

Challenge : External validity refers to the extent to which your study's findings can be generalized to other populations, settings, or conditions. Limited external validity restricts the applicability of your results and may hinder their relevance to real-world contexts.

Solution : Enhance external validity by designing studies closely resembling real-world conditions and populations of interest. Consider using diverse samples  that represent  the target population's demographic, cultural, and ecological variability. Conduct replication studies in different contexts or with different populations to assess the robustness and generalizability of your findings. Additionally, consider conducting meta-analyses or systematic reviews to synthesize evidence from multiple studies and enhance the external validity of your conclusions.

By proactively addressing these challenges and implementing effective solutions, you can strengthen the validity, reliability, and impact of your experimental research. Remember to remain vigilant for potential pitfalls throughout the research process and adapt your strategies as needed to ensure the integrity of your findings.

Advanced Topics in Experimental Research

As you delve deeper into experimental research, you'll encounter advanced topics and methodologies that offer greater complexity and nuance.

Quasi-Experimental Designs

Quasi-experimental designs resemble true experiments but lack random assignment to experimental conditions. They are often used when random assignment is impractical, unethical, or impossible. Quasi-experimental designs allow researchers to investigate cause-and-effect relationships in real-world settings where strict experimental control is challenging. Common examples include:

• Non-Equivalent Groups Design : This design compares two or more groups that were not created through random assignment. While similar to between-subjects designs, non-equivalent group designs lack the random assignment of participants, increasing the risk of confounding variables.
• Interrupted Time Series Design : In this design, multiple measurements are taken over time before and after an intervention is introduced. Changes in the dependent variable are assessed over time, allowing researchers to infer the impact of the intervention.
• Regression Discontinuity Design : This design involves assigning participants to different groups based on a cutoff score on a continuous variable. Participants just above and below the cutoff are treated as if they were randomly assigned to different conditions, allowing researchers to estimate causal effects.

Quasi-experimental designs offer valuable insights into real-world phenomena but require careful consideration of potential confounding variables and limitations inherent to non-random assignment.

Factorial Designs

Factorial designs involve manipulating two or more independent variables simultaneously to examine their main effects and interactions. By systematically varying multiple factors, factorial designs allow researchers to explore complex relationships between variables and identify how they interact to influence outcomes. Common types of factorial designs include:

• 2x2 Factorial Design : This design manipulates two independent variables, each with two levels. It allows researchers to examine the main effects of each variable as well as any interaction between them.
• Mixed Factorial Design : In this design, one independent variable is manipulated between subjects, while another is manipulated within subjects. Mixed factorial designs enable researchers to investigate both between-subjects and within-subjects effects simultaneously.

Factorial designs provide a comprehensive understanding of how multiple factors contribute to outcomes and offer greater statistical efficiency compared to studying variables in isolation.

Longitudinal and Cross-Sectional Studies

Longitudinal studies involve collecting data from the same participants over an extended period, allowing researchers to observe changes and trajectories over time. Cross-sectional studies , on the other hand, involve collecting data from different participants at a single point in time, providing a snapshot of the population at that moment. Both longitudinal and cross-sectional studies offer unique advantages and challenges:

• Longitudinal Studies : Longitudinal designs allow researchers to examine developmental processes, track changes over time, and identify causal relationships. However, longitudinal studies require long-term commitment, are susceptible to attrition and dropout, and may be subject to practice effects and cohort effects.
• Cross-Sectional Studies : Cross-sectional designs are relatively quick and cost-effective, provide a snapshot of population characteristics, and allow for comparisons across different groups. However, cross-sectional studies cannot assess changes over time or establish causal relationships between variables.

Researchers should carefully consider the research question, objectives, and constraints when choosing between longitudinal and cross-sectional designs.

Meta-Analysis and Systematic Reviews

Meta-analysis and systematic reviews are quantitative methods used to synthesize findings from multiple studies and draw robust conclusions. These methods offer several advantages:

• Meta-Analysis : Meta-analysis combines the results of multiple studies using statistical techniques to estimate overall effect sizes and assess the consistency of findings across studies. Meta-analysis increases statistical power, enhances generalizability, and provides more precise estimates of effect sizes.
• Systematic Reviews : Systematic reviews involve systematically searching, appraising, and synthesizing existing literature on a specific topic. Systematic reviews provide a comprehensive summary of the evidence, identify gaps and inconsistencies in the literature, and inform future research directions.

Meta-analysis and systematic reviews are valuable tools for evidence-based practice, guiding policy decisions, and advancing scientific knowledge by aggregating and synthesizing empirical evidence from diverse sources.

By exploring these advanced topics in experimental research, you can expand your methodological toolkit, tackle more complex research questions, and contribute to deeper insights and understanding in your field.

Experimental Research Ethical Considerations

When conducting experimental research, it's imperative to uphold ethical standards and prioritize the well-being and rights of participants. Here are some key ethical considerations to keep in mind throughout the research process:

• Informed Consent : Obtain informed consent from participants before they participate in your study. Ensure that participants understand the purpose of the study, the procedures involved, any potential risks or benefits, and their right to withdraw from the study at any time without penalty.
• Protection of Participants' Rights : Respect participants' autonomy, privacy, and confidentiality throughout the research process. Safeguard sensitive information and ensure that participants' identities are protected. Be transparent about how their data will be used and stored.
• Minimizing Harm and Risks : Take steps to mitigate any potential physical or psychological harm to participants. Conduct a risk assessment before starting your study and implement appropriate measures to reduce risks. Provide support services and resources for participants who may experience distress or adverse effects as a result of their participation.
• Confidentiality and Data Security : Protect participants' privacy and ensure the security of their data. Use encryption and secure storage methods to prevent unauthorized access to sensitive information. Anonymize data whenever possible to minimize the risk of data breaches or privacy violations.
• Avoiding Deception : Minimize the use of deception in your research and ensure that any deception is justified by the scientific objectives of the study. If deception is necessary, debrief participants fully at the end of the study and provide them with an opportunity to withdraw their data if they wish.
• Respecting Diversity and Cultural Sensitivity : Be mindful of participants' diverse backgrounds, cultural norms, and values. Avoid imposing your own cultural biases on participants and ensure that your research is conducted in a culturally sensitive manner. Seek input from diverse stakeholders to ensure your research is inclusive and respectful.
• Compliance with Ethical Guidelines : Familiarize yourself with relevant ethical guidelines and regulations governing research with human participants, such as those outlined by institutional review boards (IRBs) or ethics committees. Ensure that your research adheres to these guidelines and that any potential ethical concerns are addressed appropriately.
• Transparency and Openness : Be transparent about your research methods, procedures, and findings. Clearly communicate the purpose of your study, any potential risks or limitations, and how participants' data will be used. Share your research findings openly and responsibly, contributing to the collective body of knowledge in your field.

By prioritizing ethical considerations in your experimental research, you demonstrate integrity, respect, and responsibility as a researcher, fostering trust and credibility in the scientific community.

Conclusion for Experimental Research

Experimental research is a powerful tool for uncovering causal relationships and expanding our understanding of the world around us. By carefully designing experiments, collecting data, and analyzing results, researchers can make meaningful contributions to their fields and address pressing questions. However, conducting experimental research comes with responsibilities. Ethical considerations are paramount to ensure the well-being and rights of participants, as well as the integrity of the research process. Researchers can build trust and credibility in their work by upholding ethical standards and prioritizing participant safety and autonomy. Furthermore, as you continue to explore and innovate in experimental research, you must remain open to new ideas and methodologies. Embracing diversity in perspectives and approaches fosters creativity and innovation, leading to breakthrough discoveries and scientific advancements. By promoting collaboration and sharing findings openly, we can collectively push the boundaries of knowledge and tackle some of society's most pressing challenges.

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What is Convenience Sampling? Definition, Method, Examples

Understanding Experimental Marketing Research: A Simple Guide

Experimental marketing research is a method used by businesses to test marketing strategies and measure their impact on consumer behavior. This type of research helps companies understand how changes in marketing variables (such as price, product features, or advertising) affect customer responses. By conducting experiments, businesses can make data-driven decisions to improve their marketing efforts.

Table of Contents

Importance of experimental marketing research, why do businesses use it.

• Data-Driven Decisions : Helps businesses make informed decisions based on empirical evidence.
• Understanding Cause and Effect : Identifies how specific changes in marketing tactics influence customer behavior.
• Optimizing Marketing Strategies : Enables companies to refine their marketing strategies for better results.
• Reducing Risks : Minimizes the risks associated with new marketing initiatives by testing them before a full-scale launch.

How Experimental Marketing Research Works

Key components.

• Hypothesis : A statement predicting the outcome of the experiment. For example, “Changing the product packaging will increase sales.”
• Variables :
• Independent Variable : The factor that is changed or manipulated (e.g., packaging design).
• Dependent Variable : The outcome that is measured (e.g., sales volume).
• Control Group : A group that does not receive the experimental treatment, used as a benchmark.
• Experimental Group : A group that receives the treatment or change being tested.

Steps Involved

• Define the Objective : Clearly state what you want to learn from the experiment.
• Develop a Hypothesis : Formulate a hypothesis based on your objective.
• Design the Experiment : Decide on the variables, select control and experimental groups, and plan the method of data collection.
• Conduct the Experiment : Implement the experiment, ensuring consistent application of the independent variable.
• Collect Data : Gather data on the dependent variable.
• Analyze Results : Compare the results from the control and experimental groups to determine the impact of the independent variable.
• Draw Conclusions : Conclude whether the hypothesis was supported or not.

Examples of Experimental Marketing Research

Real-world applications.

• Product Packaging Test
• Scenario : A beverage company wants to see if changing the packaging of their soda will boost sales.
• Hypothesis : “New packaging design will increase sales by 10%.”
• Method : The company selects two similar markets. In Market A (control group), they use the existing packaging. In Market B (experimental group), they use the new packaging. Sales data is collected over a month.
• Outcome : If Market B shows a significant increase in sales compared to Market A, the company can attribute this to the new packaging.
• Price Sensitivity Experiment
• Scenario : An online retailer wants to understand how price changes affect the purchase behavior of customers.
• Hypothesis : “A 20% discount will increase the number of items purchased by 30%.”
• Method : The retailer offers a 20% discount on selected products to half of their customers (experimental group), while the other half sees regular prices (control group). Purchasing behavior is tracked over a week.
• Outcome : By comparing the number of items purchased between the two groups, the retailer can assess the impact of the discount.

Benefits of Experimental Marketing Research

Why it’s valuable.

• Precise Insights : Provides clear and precise insights into what works and what doesn’t in marketing strategies.
• Customization : Allows businesses to tailor their marketing efforts based on specific findings from experiments.
• Innovation : Encourages innovation by testing new ideas in a controlled environment.
• Competitive Advantage : Helps companies stay ahead of competitors by continuously improving their marketing tactics based on experimental findings.

Challenges and Limitations

What to watch out for.

• Cost and Time : Conducting experiments can be time-consuming and expensive.
• Complexity : Designing and implementing experiments correctly requires expertise.
• External Factors : Uncontrolled external factors can influence the results, leading to inaccurate conclusions.
• Sample Size : Small sample sizes can produce misleading results; larger samples provide more reliable data.

Experimental marketing research is a powerful tool for businesses aiming to optimize their marketing strategies through data-driven insights. By understanding and utilizing this method, companies can make more informed decisions, reduce risks, and ultimately enhance their marketing effectiveness. Whether it’s testing new packaging, pricing strategies, or advertising methods, experimental research provides a structured approach to uncovering what truly resonates with consumers. For learners in accounting and finance, grasping the basics of this research method can significantly contribute to making better business decisions and achieving marketing success.

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A guideline for designing experimental studies in marketing research and a critical discussion of selected problem areas

• Original Paper
• Published: 28 January 2014
• Volume 84 , pages 793–826, ( 2014 )

Cite this article

• Nicole Koschate-Fischer 1 &
• Stephen Schandelmeier 1  

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An Erratum to this article was published on 18 July 2014

Experiments are a well-suited method for empirically studying cause-and-effect relationships. This article provides a guideline for designing experiments. We develop an integrative framework that illustrates the key decisions in designing experimental studies. With this framework, we discuss important problem areas in the design of experiments (e.g., determining the experimental setting, choosing participants). Finally, for the different decisions and identified problem areas, we derive concrete recommendations for designing experimental studies in marketing research.

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Research Method Topics and Issues that Reduce the Value of Reported Empirical Insights in the Marketing Literatures: An Abstract

Experimental Methods

Experiments in Market Research

All independent articles serve as reference samples. Articles that exclusively refer to other papers (e.g., commentaries) were not considered. Furthermore, we did not include articles in Journal of Marketing ’s 75th Anniversary special section in the survey. The journals in Table  1 are arranged in accordance with the VHB-JOURQUAL 2.1 ( 2011 ) ranking.

We use the term ‘experimental design’ in a narrow sense as the specification of the underlying experimental plan of the investigation (Sarris 1992 ). In contrast, the expression ‘designing an experiment’ refers to all decisions made in the conception phase of an experiment (see Fig.  1 ; Christensen 2007 ; Montgomery 2009 ).

We provide an example from entrepreneurial research because we could not find an example from marketing research.

Aaker D, Kumar V, Day G, Leone R (2011) Marketing Research, 10th edn. Wiley, Hoboken

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Koschate-Fischer, N., Schandelmeier, S. A guideline for designing experimental studies in marketing research and a critical discussion of selected problem areas. J Bus Econ 84 , 793–826 (2014). https://doi.org/10.1007/s11573-014-0708-6

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How to Conduct the Perfect Marketing Experiment [+ Examples]

Updated: January 11, 2022

Published: August 30, 2021

After months of hard work, multiple coffee runs, and navigation of the latest industry changes, you've finally finished your next big marketing campaign.

Complete with social media posts, PPC ads, and a sparkly new logo, it's the campaign of a lifetime.

But how do you know it will be effective?

While there's no sure way to know if your campaign will turn heads, there is a way to gauge whether those new aspects of your strategy will be effective.

If you want to know if certain components of your campaign are worth the effort, consider conducting a marketing experiment.

Marketing experiments give you a projection of how well marketing methods will perform before you implement them. Keep reading to learn how to conduct an experiment and discover the types of experiments you can run.

What are marketing experiments?

A marketing experiment is a form of market research in which your goal is to discover new strategies for future campaigns or validate existing ones.

For instance, a marketing team might create and send emails to a small segment of their readership to gauge engagement rates, before adding them to a campaign.

It's important to note that a marketing experiment isn't synonymous with a marketing test. Marketing experiments are done for discovery, while a test confirms theories.

Why should you run a marketing experiment?

Think of running a marketing experiment as taking out an insurance policy on future marketing efforts. It’s a way to minimize your risk and ensure that your efforts are in line with your desired results.

Imagine spending hours searching for the perfect gift. You think you’ve found the right one, only to realize later that it doesn’t align with your recipient’s taste or interests. Gifts come with receipts but there’s no money-back guarantee when it comes to marketing campaigns.

An experiment will help you better understand your audience, which in turn will enable you to optimize your strategy for a stronger performance.

How to Conduct Marketing Experiments

• Brainstorm and prioritize experiment ideas.
• Find one idea to focus on.
• Make a hypothesis.
• Collect research.
• Select your metrics.
• Execute the experiment.
• Analyze the results.

Performing a marketing experiment involves doing research, structuring the experiment, and analyzing the results. Let's go through the seven steps necessary to conduct a marketing experiment.

1. Brainstorm and prioritize experiment ideas.

The first thing you should do when running a marketing experiment is start with a list of ideas.

Don’t know where to start? Look at your current priorities. What goals are you focusing on for the next quarter or the next year?

From there, analyze historical data. Were your past strategies worked in the past and what were your low performers?

As you dig into your data, you may find that you still have unanswered questions about which strategies may be most effective. From there, you can identify potential reasons behind low performance and start brainstorming some ideas for future experiments.

Then, you can rank your ideas by relevance, timeliness, and return on investment so that you know which ones to tackle first.

Keep a log of your ideas online, like Google Sheets , for easy access and collaboration.

2. Find one idea to focus on.

Now that you have a log of ideas, you can pick one to focus on.

Ideally, you organize your list based on current priorities. As such, as the business evolves, your priorities may change and affect how you rank your ideas.

Say you want to increase your subscriber count by 1,000 over the next quarter. You’re several weeks away from the start of the quarter and after looking through your data, you notice that users don’t convert once they land on your landing page.

Your landing page would be a great place to start your experiment. It’s relevant to your current goals and will yield a large return on your investment.

Even unsuccessful experiments, meaning those that do not yield expected results, are incredibly valuable as they help you to better understand your audience.

3. Make a hypothesis.

Hypotheses aren't just for science projects. When conducting a marketing experiment, the first step is to make a hypothesis you're curious to test.

A good hypothesis for your landing page can be any of the following:

• Changing the CTA copy from "Get Started" to "Join Our Community" will increase sign-ups by 5%.
• Removing the phone number field from the landing page form will increase the form completion rate by 25%.
• Adding a security badge on the landing page will increase the conversion rate by 10%.

This is a good hypothesis because you can prove or disprove it, it isn't subjective, and has a clear measurement of achievement.

A not-so-good hypothesis will tackle several elements at once, be unspecific and difficult to measure. For example: "By updating the photos, CTA, and copy on the landing page, we should get more sign-ups.

Here’s why this doesn’t work: Testing several variables at once is a no-go when it comes to experimenting because it will be unclear which change(s) impacted the results. The hypothesis also doesn’t mention how the elements would be changed nor what would constitute a win.

Formulating a hypothesis takes some practice, but it’s the key to building a robust experiment.

4. Collect research.

After creating your hypothesis, begin to gather research. Doing this will give you background knowledge about experiments that have already been conducted and get an idea of possible outcomes.

Researching your experiment can help you modify your hypothesis if needed.

Say your hypothesis is, "Changing the CTA copy from "Get Started" to "Join Our Community" will increase sign-ups by 5%." You may conduct more market research to validate your ideas surrounding your user persona and if they will resonate better with a community-focused approach.

It would be helpful to look at your competitors’ landing pages and see which strategies they’re using during your research.

5. Select your metrics.

Once you've collected the research, you can choose which avenue you will take and what metrics to measure.

For instance, if you’re running an email subject line experiment, the open rate is the right metric to track.

For a landing page, you’ll likely be tracking the number of submissions during the testing period. If you’re experimenting on a blog, you might focus on the average time on page.

It all depends on what you’re tracking and the question you want to answer with your experiment.

6. Execute the experiment.

Now it's time to create and perform the experiment.

Depending on what you’re testing, this may be a cross-functional project that requires collaborating with other teams.

For instance, if you’re testing a new landing page CTA, you’ll likely need a copywriter or UX writer.

Everyone involved in this experiment should know:

• The hypothesis and goal of the experiment
• The timeline and duration
• The metrics you’ll track

7. Analyze the results.

Once you've run the experiment, collect and analyze the results.

You want to gather enough data for statistical significance .

Use the metrics you've decided upon in the second step and conclude if your hypothesis was correct or not.

The prime indicators for success will be the metrics you chose to focus on.

For instance, for the landing page example, did sign-ups increase as a result of the new copy? If the conversion rate met or went above the goal, the experiment would be considered successful and one you should implement.

If it’s unsuccessful, your team should discuss the potential reasons why and go back to the drawing board. This experiment may spark ideas of new elements to test.

Now that you know how to conduct a marketing experiment, let's go over a few different ways to run them.

Marketing Experiment Examples

There are many types of marketing experiments you can conduct with your team. These tests will help you determine how aspects of your campaign will perform before you roll out the campaign as a whole.

A/B testing is one of the popular ways to marketing in which two versions of a webpage, email, or social post are presented to an audience (randomly divided in half). This test determines which version performs better with your audience.

This method is useful because you can better understand the preferences of users who will be using your product.

Find below the types of experiments you can run.

Your website is arguably your most important digital asset. As such, you’ll want to make sure it’s performing well.

If your bounce rate is high, the average time on page is low, or your visitors aren’t navigating your site in the way you’d like, it may be time to run an experiment.

2. Landing Pages

Landing pages are used to convert visitors into leads. If your landing page is underperforming, running an experiment can yield high returns.

The great thing about running a test on a landing page is that there are typically only a few elements to test: your background image, your copy, form, and CTA .

Experimenting with different CTAs can improve the number of people who engage with your content.

For instance, instead of using "Buy Now!" to pull customers in, why not try, "Learn more."

You can also test different colors of CTAs as opposed to the copy.

4. Paid Media Campaigns

There are so many different ways to experiment with ads.

Not only can you test ads on various platforms to see which ones reach your audience the best, but you can also experiment with the type of ad you create.

As a big purveyor of GIFs in the workplace, animating ads are a great way to catch the attention of potential customers. Those may work great for your brand.

You may also find that short videos or static images work better.

Additionally, you might run different types of copy with your ads to see which language compels your audience to click.

To maximize your return on ad spend (ROAS), run experiments on your paid media campaigns.

4. Social Media Platforms

Is there a social media site you're not using? For instance, lifestyle brands might prioritize Twitter and Instagram, but implementing Pinterest opens the door for an untapped audience.

You might consider testing which hashtags or visuals you use on certain social media sites to see how well they perform.

The more you use certain social platforms, the more iterations you can create based on what your audience responds to.

You might even use your social media analytics to determine which countries or regions you should focus on — for instance, my Twitter Analytics , below, demonstrates where most of my audience resides.

If alternatively, I saw most of my audience came from India, I might need to alter my social strategy to ensure I catered to India's time zone.

When experimenting with different time zones, consider making content specific to the audience you're trying to reach.

Your copy — the text used in marketing campaigns to persuade, inform, or entertain an audience — can make or break your marketing strategy .

If you’re not in touch with your audience, your message may not resonate. Perhaps you haven’t fleshed out your user persona or you’ve conducted limited research.

As such, it may be helpful to test what tone and concepts your audience enjoys. A/B testing is a great way to do this, you can also run surveys and focus groups to better understand your audience.

Email marketing continues to be one of the best digital channels to grow and nurture your leads.

If you have low open or high unsubscribe rates, it's worth running experiments to see what your audience will respond best to.

Perhaps your subject lines are too impersonal or unspecific. Or the content in your email is too long.

By playing around with various elements in your email, you can figure out the right strategy to reach your audience.

Ultimately, marketing experiments are a cost-effective way to get a picture of how new content ideas will work in your next campaign, which is critical for ensuring you continue to delight your audience.

Editor's Note: This post was originally published in December 2019 and has been updated for comprehensiveness.

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What is Experimental Research? Definition, Design Types & Examples

September 18, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Whether it’s running lab tests, conducting field studies, or creating simulations, experimental research is all about diving deep into unexplored territories. It’s about asking daring questions and seeking answers that push the boundaries of our knowledge. At its core, experimental research is about testing hypotheses with controlled experiments. By carefully creating conditions and collecting data, researchers can learn more about how the world works, and make sure their theories hold up.

Some of the common experimental research examples to help you understand the concept are:

• Medicinal researchers use experimental research to determine the best course of treatment for illnesses . Usually, instead of directly testing it on human subjects, they take samples of bacteria from a patient and then try the anti-bacterial medicine developed.
• Social scientists are well-versed in experimental research related to human behavior. To illustrate this, consider a study where two people are randomly chosen, and one of them is isolated from other humans for an entire year. Such scientific experiments in physical sciences allow us to gain insights into different aspects of human behavior.
• Ensuring an exceptional user experience is paramount during product development. This can be achieved by putting the product through a rigorous testing phase, where potential users are invited to give feedback and suggest improvements. Once this process is complete, the product’s final design can be released.

Table of Contents

What is Experimental Research?

Experimental research is a scientific study used to analyze and compare two sets of variables – the control group and the experimental group. For example, quantitative research techniques are classified as experimental, where the control variable is kept constant to measure changes in the experiment variable.

Experimental research refers to observing and collecting data in a controlled environment, offering potential benefits for professional use. Experimental research is a type in which researchers often test how changes to independent variables impact dependent variables. This is done by changing the value of certain independent variables and measuring the effects on corresponding dependent variables.

Key Takeaways!

• Experimental research provides a clear direction for hypothesis testing and data analysis .
• It allows for precise control over variables, resulting in highly accurate outcomes.
• Experimental research can be replicated, allowing for validation and improvement of initial findings.

Understanding Experimental Research Design

Experimental research is a great way to compare two or more variables and observe the effects on a particular group or groups under specific conditions. It helps in understanding cause-and-effect relationships of different variables like price as an independent variable affects sales which is a dependent variable. Let’s understand this with the examples of some of the independent and dependent variables –

1) Independent Variables

• Inventory (new products or upgrades)
• Sales agents’ approaches and interactions
• Digital user experience (DX
• Marketing activities
• Season, etc

2) Dependent variables

• In-store visits
• VoC feedback
• Time spent on a website
• Bounce rates
• Site traffic, etc

Importance of Experimental Design

When you conduct experimental research, it plays an important role in understanding the cause-and-effect relationships in a system. By altering particular variables and observing their effect, we can identify how different components of the system interact with one another, which is especially useful when looking into intricate matters.

It is a key component of academics & science. It’s used to test ideas , create new products, and make vital breakthroughs in different fields. Its significance goes beyond that as it’s an essential method for many scientific and academic studies. For instance, knowing the effects of a drug is integral to its development. Researching the dosage and associated side effects is an important part of this process. This enables researchers to create new drugs and treatments that are safe and effective.

Experimental research is also used in psychology to understand how the brain works and identify new treatments for mental health issues. It involves manipulating variables such as stimuli to gain insights into human behavior. It is also used in education to test new teaching methods and find the most effective ones.

Trying out something new via experimental design can be advantageous for businesses . Through experimentation, they can assess the effect of different marketing strategies , product design , and customer service on their success as well as identify potential opportunities. With this change, businesses can see if it is financially beneficial for them, as well as track how other businesses in the same industry react and if those reactions lead to an increase in sales.

When Can a Researcher Conduct Experimental Research?

Experimental research can be conducted in any environment where there is the potential for a cause and an effect. This may include the study of language, social situations, medicine, engineering, or marketing to name a few.

If it is possible to manipulate one variable while keeping the other variables constant then experimental research can be used as an effective tool for understanding how different elements interact

In order to conduct effective experimental research, a scientist must use the scientific method. This approach involves forming a hypothesis, designing an experiment, collecting data, analyzing the results and drawing conclusions based on the findings. Each step of this process requires careful consideration and planning in order for it to be successful .

You can use experimental research methods in different situations like –

• When the cause and effect are dependent on time
• When cause and effect have an invariable relationship
• When the researcher wants to understand the importance of the cause-and-effect relationship

Types of Experimental Research Design

1. pre-experimental research design.

It uses observation to observe the effects of an experiment. Pre-experimental research generally takes place in different design structures

• One-shot case study research design – Single-group pre-experimental research involves testing one group exposed to a stimulus, then analyzing the performance of individuals or entities in the group after application.
• One-group pretest-posttest design – Researchers use pre-and-post-tests to measure the effects of stimuli on test subjects. The results allow for observations about the impact of the stimulus.
• Static group comparison design – In a static group comparison, researchers assess two different groups, with only one receiving the stimuli being studied. Testing is conducted at the end of a process to compare results between groups given and not given stimuli.

2. True experimental research design

For these types of research, participants must be allocated to different groups randomly. This is known as random assignment and is a key element of the research process .

By removing bias in creating study groups, the results of the research are more reliable. Some of the design structures used in this true experimental research are-

• Posttest-only control group design – The randomized control trial design randomly divides participants into two groups, one which acts as a control and does not receive the stimuli being tested, and another which does receive the stimuli. At the conclusion of the experiment, researchers carry out tests to evaluate the tangible results arising from exposure to particular stimuli.
• Pretest-posttest control group design – It involves testing the participants before & after the stimulus from the non-control group. It utilizes a double-test model which allows researchers to compare and assess results in multiple ways.
• Solomon’s four-group design – The Solomon four-group design is a widely used experimental research structure that involves assigning participants to four randomized groups. This method is thought to be one of the most detailed and complex schemes for running experiments. Groups are formed for pre and post-tests, both with and without control groups, for research purposes.

3. Quasi-experimental research design

Quasi-experimental research is similar to true experimental research but differs in how research subjects are assigned to groups as it lacks randomization.

Instead of randomly assigned participants, they are utilized when randomization is not practical or feasible. Quasi-experimental studies are often conducted in educational settings, helping researchers to generate useful insights into the field.

How to Conduct Experimental Research

Some of the steps of conducting experimental research designs effectively are:

• Your experimental research begins with forming a question, choosing a topic, and identifying related variables.
• Then it should include both secondary sources and surveys.
• You need to collect facts and data to use when beginning a research project to help achieve more successful results.
• Once you are done with researching and examining data related to the topic, come up with a hypothesis that can be tested.
• Next, you need to identify which variables are independent and dependent. After that, choose a range for the independent variable(s) and manipulate these according to your desired outcome. You need to measure the dependent variable(s) at the time when you study the independent variable(s).
• When conducting experiments on certain topics, make sure to have a sufficient sample size and ensure that the subjects are assigned to their respective treatment groups randomly. This fairly distributes the different levels of “treatment” among them.
• Control groups are necessary for identifying how a subject would act without external influences. They provide a baseline to compare results against. This allows you to accurately compare the results with the test subjects that did receive manipulation.
• When assigning subjects for a study, you must choose between two types of groups: a completely randomized design or a randomized block design. Additionally, you can opt for either an independent measures design or a repeated measures design.
• Repetitive experimentation is essential as you need to keep trying different variable settings and monitoring the results. Additionally, make sure to document your observations by taking measurements and making notes.
• After running through the experiment(s), you can make a logical conclusion. However, it is essential to keep testing your hypothesis over time to ensure accuracy and validity.

What Data Collection Methods You can use?

While creating experimental groups for your experimental research, you can use following data collection methods:

1. Observational Study

In an observational study, a researcher records information as it naturally occurs without any intervention.

2. Simulations

Simulations involve creating a hypothetical situation to gain insight into how people or things may react in a real-world setting.

Finally, surveys rely on asking participants questions in order to obtain data.

Some of the upsides of experimental research design are –

• You can freely experiment with variables without affecting the validity of their research, plus it further offers the ability to regulate variables and enhance objectivity.
• You will be able to replicate its studies in order to examine different factors or verify their results again. This allows them to repeat experiments and gain further insight into the topic at hand.
• It can aid researchers in their understanding of different environments, providing a comprehensive overview, plus it can be deployed in a variety of industries and can help businesses gain various benefits.
• Research results can be applied to other situations and contexts, plus they can be used to validate or disprove hypotheses.
• The findings are precise and clear-cut.
• It enables the identification of correlations between different variables and their subsequent effects on one another.
• It can help researchers gain deeper insights into the relationships between different variables and understand their implications.

Disadvantages

A few downsides of an experimental research method are –

• Carrying out an activity like this requires substantial resources, time & cost, hence making it a difficult venture.
• You can create simulation models of real-world scenarios by manipulating variables, although this could lead to unintended consequences if it is not done with care.
• It is not infallible; there may be issues in the methods used and other errors that can’t be anticipated.
• Mistakes in experimental research design should be avoided to guarantee accurate results, and that may require re-running the experiment.
• Certain factors in research cannot be manipulated and some experiments may not be feasible to perform.

Experimental Research Examples

1) social media advertising on brand awareness.

This experiment will compare the effectiveness of social media campaigns on awareness with and without advertising .

The aim is to compare brand awareness between two campaigns, which could be done using surveys, interviews, or other quantitative methods. This hypothesis speculates that a campaign with creative content will lead to heightened brand recognition .

2) A/B Testing

A/B testing is a technique used to compare two versions of a campaign; it can be used to test whether more creative content will have a greater impact on brand awareness .

Two versions of an ad campaign were created: a creative version and a less creative version. The effectiveness of each was assessed to determine which one generated more brand awareness.

Conclusion!

By using experimental research techniques , scientists are able to test theories and identify the root cause of an issue. Furthermore, experimental studies provide more reliable results compared to observational ones. Additionally, quantitative data collected from experiments can be processed & analyzed more accurately than qualitative data.

Applied to cause and effect studies, this type of research method is incredibly valuable. It enables researchers to modify certain variables and track their impact on a predetermined dependent variable, thus giving them deep insights into the relationship between the two.

Despite the positive effects it can have, experimental research also comes with certain drawbacks. Namely, researchers can be biased in their data collection, it’s often difficult to replicate results, and a large number of sample sizes are required for output accuracy.

1) What is experimental research and example?

Experimental research is a method of scientific investigation in which an investigator manipulates certain variables and measures their effect on other variables. For example, a researcher studying the effects of caffeine on concentration might administer coffee to one group of subjects and a placebo to another, then measure their relative concentration levels.

2) What are characteristics of an experiment in research?

Key characteristics of experimental research include control over variables, the ability to manipulate variables to measure their effects, random assignment of subjects to treatments, and the use of control groups.

3) What are the two basic types of experimental research?

The two basic types of experimental research are true experiments and quasi-experiments. True experiments involve random assignment of subjects to different conditions, while quasi-experiments do not.

4) What is the format of experimental research?

The standard format of experimental research includes an introduction (with hypothesis), methodology (including the description of participants, materials, and procedures), results section (presenting the findings), and a conclusion (discussing the results and their implications).

Liked this post? Check out the complete series on Market research

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• Qualitative Research: Meaning, and Features of Qualitative Research
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About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Very thoughtful analysis of the potential benefits that come with including an appropriate ratio of experimental research. Thanks for sharing.

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If there's one thing to be sure about marketing, it is the fact that it can burn a lot of money. A Gartner study reveals businesses spend roughly 12 percent of their revenue on advertising and marketing. 8   That's $1.20 out of every $10 they make! Luckily, marketing doesn't always have to be wasteful. In fact, if marketers know where and how to invest their money, they can save the company tons of unwanted costs while maximizing ROI. This is the reason for experimental research. Let's dive in and learn all about this research method.   

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A company changes its packaging but keeps everything the same to observe the impact of the new packaging on sales. This is an example of _____ experimental research. 

A bakery changes the amount of flour in its bread to observe customer reactions. This is an example of ______ experimental research. 

Which research method provides an accurate description of the current state?

The descriptive research method can assess the causal relationship among variables. 

Which research methods are used to understand the relationship between variables? (More than one answers are possible)

In quasi-experimental research, the variation is caused by _________.

Experimental research mostly relies on guesswork, not actual data from real experience. 

A variable  is a value that _______.

A variable that depends on another variable is called _________.

Correlational research allows the manipulation of variables. 

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Experimental Research in Marketing

Before we learn what experimental research means, we must first understand why it is adopted. To do so, let's go back to two fundamental concepts: Mass marketing and Targeted marketing.

Mass marketing is a strategy where companies ignore the differences between market segments and try to reach as many people as possible.

Unfortunately, this is rather expensive and only affordable by prominent firms. For smaller brands, a better way to access customers is through targeted marketing:

Targeted marketing means breaking the market into smaller segments and focusing marketing efforts on them individually.

However, even when companies know what customers to target, it remains a challenge to capture their attention. How to know what the audience is interested in and craft a marketing message that will appeal to them? This is where experimental research comes in.

Experimental Research Definition

Experimental research is a test of which marketing strategy is most likely to work. But rather than guesswork, it uses real data from experiments.

Experimental research involves making hypotheses about what marketing activity is likely to appeal to customers and collecting data to see if it is true.

One key feature of experimental research is that it explores the relationship between independent and dependent variables.

• Independent variables are variables that do not depend on other variables.
• Dependent variables are variables that change as independent variables change.

In experimental research, the independent variable is the cause and the dependent variable is the effect. As a marketer, you can influence the cause to observe changes in the effect, but not the other way around. The effect can only be tested or measured.

This is why experimental research is so powerful. It not only allows you to see the immediate results of a marketing decision but also manipulates the cause to influence the result.

When you're confused about independent and dependent variables, ask these three questions:

Remember earlier we mentioned experimental research examines the relationship of independent and dependent variables, can you guess what this relationship is called? (Hint: an independent variable is a cause and a dependent variable effect.)

Did you say it is a cause-and-effect relationship? Spot on! We don't have to dive deeper into this right now. Just keep in mind that in a cause-and-effect relationship, one event takes place before another, and the second event can't happen before the first. For instance, you must add or remove a menu item to see how it affects sales revenue.

Experimental Research Example

Suppose you start an ice cream stall in the summer and decide to try out different ice cream flavors to see which one performs best. In this case, the ice cream flavor is the independent variable, and the ice cream sale revenue affected by the change of flavor is a dependent variable.

Note here that the independent variable is the cause while the dependent variable is the effect . It is the change in the ice cream flavor that leads to the change in ice cream sales. While you can control which ice cream flavor to sell, you can't know which flavor will bring you the most profit without experimenting. This brings us back to a point early on: A cause can be manipulated to test or measure the effect.

Types of Experimental Research

When it comes to experimental research, there are three main types: controlled, manipulated, and random.

Controlled experimental research - Research where all outside factors are kept constant. Only the measured variable is changed. For example, a hamburger store changes its packaging while keeping everything else (ingredients, flavor, etc.) the same to observe the effect of the new packaging on sales.

Manipulated experimental research - Research where you can change the independent variable to measure the effect on the dependent variable. For instance, a bakery changes the amount of flour in bread and sees how customers respond.

Random experimental research - A combination of the above two. For example, you add a new drink to the menu of all coffee stores you own, with everything remaining the same. If you randomly check a store, you will see that the sales may go up or down depending on the location.

The experimental research method may be different, but the idea is always the same, to find out the strategy that helps the business improve its performance.

Descriptive vs Correlational vs Experimental Research

Have you heard of descriptive and correlational research before? If you have, you might also know that they are human behavior research methods just like experimental research. Don't worry if they can't tell the difference between them. This section will help clearly distinguish these three types of research methods.

Let's start with the definitions:

Descriptive research is the type of research that provides an accurate description of a situation. As a result, it is often used to discover information, make predictions, and test a hypothesis. The only drawback is that it does not explain the relationship between different variables.

Correlational research is also used to test hypotheses and make predictions. However, unlike descriptive research, it allows the researcher to observe the causal relationship between variables.

Experimental research is similar to correlational research but goes one step further by letting the researcher manipulate the variable to see different results. 1

Here's a more detailed comparison table:

Table 2. Descriptive vs Correlational vs Experimental Research. Source: Openpress.

Difference between Correlational and Experimental Research

Correlational research shows only the association between two variables, not necessarily the cause-and-effect relationship between them. Experimental research, on the other hand, allows the marketer to manipulate the independent variable to measure its effect on the dependent variable.

Correlational research only answers the question: Is there a positive or negative correlation between two variables? Experimental research goes one step further by measuring the impact of this correlation.

Suppose a restaurant wants to observe the effect of a new sauce recipe on sales. If using correlational research , the restaurant owner can only observe the relationship between one type of sauce and sales at a time. With experimental research , the owner can try out different types of sauce for a week and see which one drives the most sales and adds it to the menu.

Quasi-Experimental Research

Marketers use quasi-experimental research to understand the change in customer or firm behavior. According to Campell, this is the type of research where the " data generating process is not intentionally experimental" but caused by an external shock. 6

In quasi-experimental research , an external shock causes a variation that the researcher will use to study its impact on a situation.

Quasi-experimental research of eBay : The company paused advertising on Bing and saw little traffic loss. This inspired a follow-up experiment where the company randomly stopped paid search advertising and discovered similar results. In this example, the Quasi-experiment was used to study the consequence of the company's actions. 7

Experimental Research - Key Takeaways

• Experimental research conducts experiments to determine which marketing activity appeals to customers.

Experimental research is based on actual data and real-life situations rather than guesswork by the researcher.

Besides experimental research, there are two other approaches to studying human behavior - descriptive and correlational research.

Experimental research differs from other types of research in that it studies the causal relationship between independent and dependent variables.

Quasi-experimental research involves researching the impact of a variation caused by an external shock on a situation. Marketers use quasi-experimental research to understand the change in customer or firm behavior.

• Charles Stangor & Jennifer Walinga, Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour, n.d., https://openpress.usask.ca/introductiontopsychology/chapter/psychologists-use-descriptive-correlational-and-experimental-research-designs-to-understand-behavior/
• Know This, Descriptive Market Research, n.d., https://www.knowthis.com/planning-for-marketing-research/descriptive-market-research/
• Know This, Causal Market Research, n.d., https://www.knowthis.com/planning-for-marketing-research/causal-market-research/
• Stangor, Research methods for the behavioural sciences (4th ed.), 2011.
• Pritha Bhandari, Independent vs. Dependent Variables | Definition & Examples, 2022.
• Campbell, Roy H., A Managerial Approach to Advertising Measurement, Journal of Marketing, 1965.
• Blake, Thomas, Nosko, Chris, Tadelis, Steven, Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment, Econometrica, 83 (1), 155–74, 2015.
• Tanya Castaneda, How Successful Companies Spend Their Advertising Budget, 2018, https://mediamaxnetwork.com/blog/how-successful-companies-spend-their-advertising-budget/.

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Descriptive research method

Correlational research 

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Frequently Asked Questions about Experimental Research

What is experimental research?

Experimental research involves testing and analysing marketing variables changes to determine which marketing activity will appeal to customers. 

What is quasi-experimental research?

Quasi-experimental research is the research on how a variable caused by an external shock should impact the firm's performance. It is mainly used to understand the consequences of customer or firm behaviour changes. 

What is the purpose of experimental research?

The purpose of experimental research is to find out which marketing activity will have the most positive impact on the business. It also helps marketers learn about customers' needs and develop offerings that match their expectations. 

What are the types of experimental research?

Experimental research can be controlled, manipulated, or random. The controlled experimental research is conducted where all outside factors are kept the same. Only the measured variable is changed. The manipulated experimental research involves changing the variable to obtain the desired results. Random experimental research is the combination of the above two. 

What is the difference between correlational and experimental research?

Correlational research shows only the association between two variables, not necessarily the cause and effect relationship between them. In experimental research, you can manipulate the independent variable to measure its effect on the dependent variable. In correlational research, you must keep everything as it is.

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Experimental Research Design — 6 mistakes you should never make!

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

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

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

Table of Contents

What Is Experimental Research Design?

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

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

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

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

Importance of Experimental Research Design

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

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

Types of Experimental Research Designs

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

1. Pre-experimental Research Design

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

Pre-experimental research is of three types —

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

2. True Experimental Research Design

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

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

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

3. Quasi-experimental Research Design

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

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

Advantages of Experimental Research

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

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

6 Mistakes to Avoid While Designing Your Research

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

1. Invalid Theoretical Framework

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

2. Inadequate Literature Study

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

3. Insufficient or Incorrect Statistical Analysis

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

4. Undefined Research Problem

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

5. Research Limitations

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

6. Ethical Implications

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

Experimental Research Design Example

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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• What is experimental research: Definition, types & examples

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!

• What is experimental research?

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.

• 3 Types of experimental research designs

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

1 - Pre-experimental design

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:

A. One shot case study research design

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.

B. One group pre-test post-test research design

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. 

C. Static-group comparison

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.

2 - Quasi-experimental design

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 designs

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.

B. Regression discontinuity

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.

C. Natural Experiments

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.

3 - True experimental design

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:

A. Post-test only control group design

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.

B. Pre-test post-test control group

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.

C. Solomon four-group design

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.

• Advantages and disadvantages of experimental research

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:

Advantages of experimental research:

• All the variables are in the researchers’ control, and that means the researcher can influence the experiment according to the research question’s requirements.
• As you can easily control the variables in the experiment, you can specify the results as much as possible.
• The results of the study identify a cause-and-effect relation .
• The results can be as specific as the researcher wants.
• The result of an experimental design opens the doors for future related studies.

Disadvantages of experimental research:

• Completing an experiment may take years and even decades, so the results will not be as immediate as some of the other research types.
• As it involves many steps, participants, and researchers, it may be too expensive for some groups.
• The possibility of researchers making mistakes and having a bias is high. It is important to stay impartial
• Human behavior and responses can be difficult to measure unless it is specifically experimental research in psychology.
• Examples 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:

An example of experimental research in science:

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.

An example of experimental research in marketing:

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 .

• Frequently asked questions about experimental research

Is experimental research qualitative or quantitative?

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.

What is the difference between quasi-experimental research and experimental research?

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.

• Wrapping it up

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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The Benefits of Experimental Design in Marketing vs. A/B Testing

I. Don’t. Know. Those may be the scariest three words in marketing. No one wants to admit to their boss that they aren’t sure whether a campaign will perform with a particular customer segment on a given channel. Yet even the most experienced marketers can’t know for sure if a campaign will perform. Customer needs simply change too quickly. A marketer’s judgment may also be clouded by their own personal experiences, preferences, and biases. That is why A/B testing has been embraced by digital marketers. Yet we can do even better. The advent of AI and data analytics allows marketers to upgrade their testing approach to use experimental design in marketing. Let’s explore why you should and how to do it.

What is experimental design?

Experimental design is a research, testing, and optimization approach used to organize data and run statistical experiments with it. With experimental design, you can identify cause-and-effect relationships. In marketing, that means you can see how your campaigns (cause) produce customer engagement (effect).

How can we use experimental design in marketing?

Using experimental design in marketing lets brands identify which elements of their marketing messages connect for customers and which ones fall flat. It does that with an approach that separately tags each element of a campaign to isolate data related to it. For example, you can tag the different words or phrases that make up an email subject line or the copy on a web banner ad.

After tagging each element separately, the brand can develop a hypothesis about what appeals to their customers. Then run an experiment in which they present different versions of the same campaign to gauge real-world reactions. The results allow marketers to see how customers respond to each version overall and to each of its separate components. In practice, that means you only need to run one experiment to get a slew of insights into what makes your customers take action.

Using experimental design in this way lets a brand efficiently test different versions of a tagline, image, description, and call-to-action button—all at once. Digital marketers can test multiple ideas and then adjust campaigns so that the version they put into market has the highest potential for customer engagement. Only by using experimental design in marketing can brands say with absolute certainty, “I know this will work.”

What are the limitations of experimental design?

Experimental design in marketing brings deep insights with extreme efficiency. Yet that experiment still needs a large enough sample size to reach significance. The exact size depends on the number of elements and variants you are including in the experiment. If there are a lot of them and your organization has limited digital traffic reaching significance can take…awhile. Some marketers would rather just go with their instincts rather than wait for statistically valid results.

Experimental design also requires specific technology designed for the purpose. 

How does experimental design differ from A/B testing?

As the name implies, A/B tests allow brands to test an A version and a B version of a message. Some marketers refer to an A/B test as a champion versus challenger experiment. These typically require marketers to randomly assign members of their audience into two groups. Each sees one or the other message. The engagement results tell the marketers which version performed better than the other in its entirety. 

Note, marketers can run these kinds of tests with more than two variants. They just divide the audience randomly by the number of variants you want to compare.

For brands that only want to test two, three, or four variations of a single element, an A/B test can produce a usable result with a modest sample size. That ease has made A/B testing popular.

What are the limitations of A/B testing?

A/B tests have limitations in today’s climate of massive scale and real-time interactivity. Chief among them is that A/B tests cannot efficiently test multiple versions of multiple page elements against one another. A marketer would have to run a separate A/B test for each element and each version they want to assess. That would quickly produce an unmanageable number of tests. Each test would need a large enough audience to produce a statistically valid result.

With a combination of experimental design and data analytics, in contrast, a marketer could gather data on multiple versions and multiple elements with just one experiment.

There’s another limitation to A/B testing that may be even more important. It’s that A/B tests can’t tell you why . Because they only show the results of one full message against another full message, they don’t reveal the impact that subtle changes can have.

That’s a problem for modern marketers. Knowing why some messages work and some fail is critical to improving marketing results over time. This is especially true when trying to optimize results with segment marketing.

Despite those limitations, A/B testing is still a valuable and valid form of testing. This is especially true for marketing teams that want test results in just a few hours. Or for those with too little data to employ experimental design. 

Experimental design in action

To illustrate experimental design in marketing, consider how Persado does it.

When a Persado customer plans a marketing campaign and wants to run an experiment, the brand’s human creators craft the message and then give it to us. We run it through the Persado Motivation AI Platform , which analyzes what the message is trying to motivate people to do. 

Persado is an enterprise Generative AI solution built on a large language model and knowledge base containing more than ten years of high-performing campaign language from Fortune 500 firms.

Once the Persado Motivation AI has analyzed the purpose of our customer’s campaign, it then generates alternative options. Many or most will be predicted to out-perform our customer’s human-generated original. By “out-perform” we mean the message will result in more clicks, more click-throughs, more conversions, fewer abandoned carts , etc.

Sometimes, our clients simply choose one of those predictive alternatives and use it. But if they want to be 100% sure they have the best performer, they run a language experiment using experimental design. The point is to measure how real consumers respond to the different messages. An experiment can include as few as four and as many as 16 versions of the message.

The data that come out of those experiments show which messages perform the best with which consumers—and why. The Persado AI can see how each version performed as a whole. It also shows the impact of each element of the message.

For example, we can see how much impact the subject line or CTA had on the overall message lift. Think of those elements as sources of motivation.

Experimental design in marketing allows marketers to compare multiple messages and elements of messages at the same time.

Using an A/B testing approach would require Persado to create a different campaign to test every message element against every other message element. It would require hundreds of tests for one campaign. Experimental design, in contrast, only requires one test. The findings on each element are arranged in a way that allows the AI to quickly predict how they perform in different combinations, so you can get the best campaign in front of your customers quickly.

How luxury retail company Tapestry leverages experimental design with Persado

Luxury fashion house Tapestry works with Persado to run digital experiments for its brands. These include Coach, Kate Spade, and Stuart Weitzman. E-commerce leaders at Tapestry sat down with Persado to talk about how they leverage experimental design, Generative AI and brand language to optimize customer engagement.

Listen to their conversation on-demand here .

Best observed vs. best predicted outcomes in experimental design

One interesting additional element of how Persado conducts experimental design is through “best observed” and “best predicted” outcomes. The best observed option is the one that Persado put into the market. But even with experimental design, testing all the elements and variables to get an observed outcome could take time.

The best predicted outcome, in contrast, could be headline A along with body copy B, image C and call-to-action D. That one may not have appeared in the experiment. The machine learning algorithm can predict it will perform the best based on the experimental results. If the brand agrees, Persado can then create the best predicted version and broadcast it to the customer’s audience.

In this way, experimental design provides more insights to marketers in less time with less work than would be required using A/B testing.

How can experimental design help businesses grow revenue?

Potential customers often interact with a brand multiple times before they decide to make a purchase. That means that every opportunity to communicate and engage with people counts. If a brand can increase the number of people who respond to their messages, they increase conversions. 

Knowing which messages resonate, which do not, and why, gets to the heart of competing on customer experience.

A Persado banking customer offers a case in point. The bank engaged Persado to improve the impact of a website campaign. Yet the experiment found no difference in click-through rates across the various messages compared to the control.

That was an unusual result and difficult to understand. That is, until the Persado team discovered the impact that the customer journey could have. People who clicked to the website from an email engaged at one rate. People who clicked through from social media engaged at another.

The differing responses canceled each other out when viewed in aggregate. But, when Persado designed an experiment to test different messages for different audiences on different channels, the performance improved, leading to better conversions. Most importantly, the bank’s marketing leaders understood why people engaged and converted on the site and used those insights to inform other campaigns.

Listen to leaders from Chase and Persado talk about these findings.

Such is the promise and the result of experimental design.

Find out more about how Persado Motivation AI merges our capabilities with experimental design and Generative AI to help brands deliver marketing impact. Reach out about our risk-free trial options today .

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10 Real-Life Experimental Research Examples

Chris Drew (PhD)

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

Learn about our Editorial Process

Experimental research is research that involves using a scientific approach to examine research variables.

Below are some famous experimental research examples. Some of these studies were conducted quite a long time ago. Some were so controversial that they would never be attempted today. And some were so unethical that they would never be permitted again.

A few of these studies have also had very practical implications for modern society involving criminal investigations, the impact of television and the media, and the power of authority figures.

Examples of Experimental Research

1. pavlov’s dog: classical conditioning.

Dr. Ivan Pavlov was a physiologist studying animal digestive systems in the 1890s. In one study, he presented food to a dog and then collected its salivatory juices via a tube attached to the inside of the animal’s mouth.

As he was conducting his experiments, an annoying thing kept happening; every time his assistant would enter the lab with a bowl of food for the experiment, the dog would start to salivate at the sound of the assistant’s footsteps.

Although this disrupted his experimental procedures, eventually, it dawned on Pavlov that something else was to be learned from this problem.

Pavlov learned that animals could be conditioned into responding on a physiological level to various stimuli, such as food, or even the sound of the assistant bringing the food down the hall.

Hence, the creation of the theory of classical conditioning. One of the most influential theories in psychology still to this day.

2. Bobo Doll Experiment: Observational Learning

Dr. Albert Bandura conducted one of the most influential studies in psychology in the 1960s at Stanford University.

His intention was to demonstrate that cognitive processes play a fundamental role in learning. At the time, Behaviorism was the predominant theoretical perspective, which completely rejected all inferences to constructs not directly observable .

So, Bandura made two versions of a video. In version #1, an adult behaved aggressively with a Bobo doll by throwing it around the room and striking it with a wooden mallet. In version #2, the adult played gently with the doll by carrying it around to different parts of the room and pushing it gently.

After showing children one of the two versions, they were taken individually to a room that had a Bobo doll. Their behavior was observed and the results indicated that children that watched version #1 of the video were far more aggressive than those that watched version #2.

Not only did Bandura’s Bobo doll study form the basis of his social learning theory, it also helped start the long-lasting debate about the harmful effects of television on children.

Worth Checking Out: What’s the Difference between Experimental and Observational Studies?

3. The Asch Study: Conformity  

Dr. Solomon Asch was interested in conformity and the power of group pressure. His study was quite simple. Different groups of students were shown lines of varying lengths and asked, “which line is longest.”

However, out of each group, only one was an actual participant. All of the others in the group were working with Asch and instructed to say that one of the shorter lines was actually the longest.

Nearly every time, the real participant gave an answer that was clearly wrong, but the same as the rest of the group.

The study is one of the most famous in psychology because it demonstrated the power of social pressure so clearly.  

4. Car Crash Experiment: Leading Questions

In 1974, Dr. Elizabeth Loftus and her undergraduate student John Palmer designed a study to examine how fallible human judgement is under certain conditions.

They showed groups of research participants videos that depicted accidents between two cars. Later, the participants were asked to estimate the rate of speed of the cars.

Here’s the interesting part. All participants were asked the same question with the exception of a single word: “How fast were the two cars going when they ______into each other?” The word in the blank varied in its implied severity.

Participants’ estimates were completely affected by the word in the blank. When the word “smashed” was used, participants estimated the cars were going much faster than when the word “contacted” was used. 

This line of research has had a huge impact on law enforcement interrogation practices, line-up procedures, and the credibility of eyewitness testimony .

5. The 6 Universal Emotions

The research by Dr. Paul Ekman has been influential in the study of emotions. His early research revealed that all human beings, regardless of culture, experience the same 6 basic emotions: happiness, sadness, disgust, fear, surprise, and anger.

In the late 1960s, Ekman traveled to Papua New Guinea. He approached a tribe of people that were extremely isolated from modern culture. With the help of a guide, he would describe different situations to individual members and take a photo of their facial expressions.

The situations included: if a good friend had come; their child had just died; they were about to get into a fight; or had just stepped on a dead pig.

The facial expressions of this highly isolated tribe were nearly identical to those displayed by people in his studies in California.

6. The Little Albert Study: Development of Phobias  

Dr. John Watson and Dr. Rosalie Rayner sought to demonstrate how irrational fears were developed.

Their study involved showing a white rat to an infant. Initially, the child had no fear of the rat. However, the researchers then began to create a loud noise each time they showed the child the rat by striking a steel bar with a hammer.

Eventually, the child started to cry and feared the white rat. The child also developed a fear of other white, furry objects such as white rabbits and a Santa’s beard.

This study is famous because it demonstrated one way in which phobias are developed in humans, and also because it is now considered highly unethical for its mistreatment of children, lack of study debriefing , and intent to instil fear.  

7. A Class Divided: Discrimination

Perhaps one of the most famous psychological experiments of all time was not conducted by a psychologist. In 1968, third grade teacher Jane Elliott conducted one of the most famous studies on discrimination in history. It took place shortly after the assassination of Dr. Martin Luther King, Jr.

She divided her class into two groups: brown-eyed and blue-eyed students. On the first day of the experiment, she announced the blue-eyed group as superior. They received extra privileges and were told not to intermingle with the brown-eyed students.

They instantly became happier, more self-confident, and started performing better academically.

The next day, the roles were reversed. The brown-eyed students were announced as superior and given extra privileges. Their behavior changed almost immediately and exhibited the same patterns as the other group had the day before.

This study was a remarkable demonstration of the harmful effects of discrimination.

8. The Milgram Study: Obedience to Authority

Dr. Stanley Milgram conducted one of the most influential experiments on authority and obedience in 1961 at Yale University.

Participants were told they were helping study the effects of punishment on learning. Their job was to administer an electric shock to another participant each time they made an error on a test. The other participant was actually an actor in another room that only pretended to be shocked.

However, each time a mistake was made, the level of shock was supposed to increase, eventually reaching quite high voltage levels. When the real participants expressed reluctance to administer the next level of shock, the experimenter, who served as the authority figure in the room, pressured the participant to deliver the next level of shock.

The results of this study were truly astounding. A surprisingly high percentage of participants continued to deliver the shocks to the highest level possible despite the very strong objections by the “other participant.”

This study demonstrated the power of authority figures.

9. The Marshmallow Test: Delay of Gratification

The Marshmallow Test was designed by Dr. Walter Mischel to examine the role of delay of gratification and academic success.

Children ages 4-6 years old were seated at a table with one marshmallow placed in front of them. The experimenter explained that if they did not eat the marshmallow, they would receive a second one. They could then eat both.

The children that were able to delay gratification the longest were rated as significantly more competent later in life and earned higher SAT scores than children that could not withstand the temptation.  

The study has since been conceptually replicated by other researchers that have revealed additional factors involved in delay of gratification and academic achievement.

10. Stanford Prison Study: Deindividuation

Dr. Philip Zimbardo conducted one of the most famous psychological studies of all time in 1971. The purpose of the study was to investigate how the power structure in some situations can lead people to behave in ways highly uncharacteristic of their usual behavior.

College students were recruited to participate in the study. Some were randomly assigned to play the role of prison guard. The others were actually “arrested” by real police officers. They were blindfolded and taken to the basement of the university’s psychology building which had been converted to look like a prison.

Although the study was supposed to last 2 weeks, it had to be halted due to the abusive actions of the guards.

The study demonstrated that people will behave in ways they never thought possible when placed in certain roles and power structures. Although the Stanford Prison Study is so well-known for what it revealed about human nature, it is also famous because of the numerous violations of ethical principles.

The studies above are varied and focused on many different aspects of human behavior . However, each example of experimental research listed above has had a lasting impact on society. Some have had tremendous sway in how very practical matters are conducted, such as criminal investigations and legal proceedings.

Psychology is a field of study that is often not fully understood by the general public. When most people hear the term “psychology,” they think of a therapist that listens carefully to the revealing statements of a patient. The therapist then tries to help their patient learn to cope with many of life’s challenges. Nothing wrong with that.

In reality however, most psychologists are researchers. They spend most of their time designing and conducting experiments to enhance our understanding of the human condition.

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Bandura A. (1965). Influence of models’ reinforcement contingencies on the acquisition of imitative responses. Journal of Personality and Social Psychology, 1 (6), 589-595. https://doi.org/doi:10.1037/h0022070

Beck, H. P., Levinson, S., & Irons, G. (2009). Finding little Albert: A journey to John B. Watson’s infant laboratory.  American Psychologist, 64(7),  605-614.

Ekman, P. & Friesen, W. V. (1971).  Constants Across Cultures in the Face and motion .  Journal of Personality and Social Psychology, 17(2) , 124-129.

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of

the interaction between language and memory. Journal of Verbal Learning and Verbal

Behavior, 13 (5), 585–589.

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Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification . Journal of Personality and Social Psychology, 16 (2), 329-337.

Pavlov, I.P. (1927). Conditioned Reflexes . London: Oxford University Press.

Watson, J. & Rayner, R. (1920). Conditioned emotional reactions.  Journal of Experimental Psychology, 3 , 1-14. Zimbardo, P., Haney, C., Banks, W. C., & Jaffe, D. (1971). The Stanford Prison Experiment: A simulation study of the psychology of imprisonment . Stanford University, Stanford Digital Repository, Stanford.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
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The Benefits of Quasi-Experiments in Marketing [Research Methodology]

Avi Goldfarb, Catherine Tucker and Yanwen Wang

Quasi-experimental methods have been widely applied in marketing to explain changes in consumer behavior, firm behavior, and market-level outcomes. The purpose of quasi-experimental methods is to determine the presence of a causal relationship in the absence of experimental variation. A new Journal of Marketing article offers guidance on how to successfully conduct research in marketing with quasi-experiments to understand whether an action causally affects a marketing outcome.

As a vivid example, we describe a quasi-experiment that occurred when eBay shut down all the paid search advertising on Bing during a dispute with Microsoft, but lost little traffic. These quasi-experimental results inspired a follow-up field experiment where eBay randomized suspension of its branded paid search advertising and found results consistent with the quasi-experiment.   We begin by establishing various type of quasi-experimental variation at the individual, organizational, and market-levels. In each type, given the lack of an experiment, some individuals, companies, or markets receive an action or policy (i.e., treatment group) and some do not (i.e., control group). For example, some markets are affected by a new policy and some are not. The question is how the markets receiving the treatment would act if they had not received it (i.e., the counterfactual). The unobservability of the counterfactual means assumptions are required to ensure that differences (both observed and unobserved) are as untroubling as possible, thereby mimicking random assignment as closely as possible. 

We discuss how to structure an empirical strategy to identify a causal relationship using methods such as difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. We emphasize the importance of clearly communicating identifying assumptions underlying the assertion of causality and establishing the generalizability of the findings.

We examine the following topics with the goal of helping researchers and analysts use quasi-experiments more effectively.

• Topic 1 – Research question: Do we care whether x causes y? The first and hardest stage in this process is identifying a question in which marketing scholars, managers, or policy makers actually care whether x causes y.
• Topic 2 – Data question: How to find data with quasi-experimental variation in x? Much of the work using quasi-experimental variations in marketing harnesses easily understood events such as contract changes; ecological issues such as the weather; geography; and macroeconomic, individual, organizational, and regulatory changes. The key is to consider why each of these sources of variation can approximate random assignment.
• Topic 3 – Identification strategy: Does x cause y to change? The researcher must first explain where the variation claimed to be exogenous comes from. Second, the researcher needs to demonstrate that the relationship between the variation and the outcome of interest is very likely driven by the relationship between x and y and not by some other factor.
• Topic 4 – Empirical analysis: How to estimate the effect of x on y? We discuss three different regression analysis frameworks using quasi-experiments: difference-in-differences, regression discontinuity, and instrumental variables.
• Topic 5 – Challenges to research question: What if variation in x is not exogenous? We outline three methods—propensity score matching, synthetic control methods, and selection bias correction—with steps to take when comparability between the control and treatment groups is violated.
• Topic 6 – Robustness: How robust is the effect of x on y? The idea here is to show that the sign, significance, and magnitude of the estimate remain broadly consistent across a vast range of possible models. A few examples of robustness checks include different controls, functional forms, choices of time periods, dependent variables, the size of the control group, and a placebo test.
• Topic 7 – Mechanism: Why does x cause y to change? Understanding how the process of the change unfolds adds insight that can drive new knowledge and stronger actions.
• Topic 8 – External validity: How generalizable is the effect of x on y? The external validity discussion in a paper should recognize the assumptions required for the analysis to capture the average treatment effect across the population of interest rather than a more local effect that is an artifact of the data sample or the source of quasi-experimental variation.
• Topic 9 – Apologies: What remains unproven and what are the caveats? Any identification strategy relies on assumptions that need to be explicit throughout the paper. While apologies do not mean all is forgiven, the objective should be to clarify the boundaries of claims made in the paper.

Read the full article .

From: Avi Goldfarb, Catherine Tucker, and Yanwen Wang, “ Conducting Research in Marketing with Quasi-Experiments ,” Journal of Marketing .

Go to the Journal of Marketing

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Avi Goldfarb is Ellison Professor of Marketing, University of Toronto, Canada.

Catherine Tucker is Sloan Distinguished Professor of Management Science, Massachusetts Institute of Technology, USA.

Yanwen Wang is Assistant Professor of Marketing, University of British Columbia, Canada.

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If there's one thing to be sure about marketing, it is the fact that it can burn a lot of money. A Gartner study reveals businesses spend roughly 12 percent of their revenue on advertising and marketing. 8   That's $1.20 out of every $10 they make! Luckily, marketing doesn't always have to be wasteful. In fact, if marketers know where and how to invest their money, they can save the company tons of unwanted costs while maximizing ROI. This is the reason for experimental research. Let's dive in and learn all about this research method.   

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A company changes its packaging but keeps everything the same to observe the impact of the new packaging on sales. This is an example of _____ experimental research. 

A bakery changes the amount of flour in its bread to observe customer reactions. This is an example of ______ experimental research. 

Which research method provides an accurate description of the current state?

The descriptive research method can assess the causal relationship among variables. 

Which research methods are used to understand the relationship between variables? (More than one answers are possible)

In quasi-experimental research, the variation is caused by _________.

Experimental research mostly relies on guesswork, not actual data from real experience. 

A variable  is a value that _______.

A variable that depends on another variable is called _________.

Correlational research allows the manipulation of variables. 

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Experimental Research in Marketing

Before we learn what experimental research means, we must first understand why it is adopted. To do so, let's go back to two fundamental concepts: Mass marketing and Targeted marketing.

Mass marketing is a strategy where companies ignore the differences between market segments and try to reach as many people as possible.

Unfortunately, this is rather expensive and only affordable by prominent firms. For smaller brands, a better way to access customers is through targeted marketing:

Targeted marketing means breaking the market into smaller segments and focusing marketing efforts on them individually.

However, even when companies know what customers to target, it remains a challenge to capture their attention. How to know what the audience is interested in and craft a marketing message that will appeal to them? This is where experimental research comes in.

Experimental Research Definition

Experimental research is a test of which marketing strategy is most likely to work. But rather than guesswork, it uses real data from experiments.

Experimental research involves making hypotheses about what marketing activity is likely to appeal to customers and collecting data to see if it is true.

One key feature of experimental research is that it explores the relationship between independent and dependent variables.

• Independent variables are variables that do not depend on other variables.
• Dependent variables are variables that change as independent variables change.

In experimental research, the independent variable is the cause and the dependent variable is the effect. As a marketer, you can influence the cause to observe changes in the effect, but not the other way around. The effect can only be tested or measured.

This is why experimental research is so powerful. It not only allows you to see the immediate results of a marketing decision but also manipulates the cause to influence the result.

When you're confused about independent and dependent variables, ask these three questions:

Remember earlier we mentioned experimental research examines the relationship of independent and dependent variables, can you guess what this relationship is called? (Hint: an independent variable is a cause and a dependent variable effect.)

Did you say it is a cause-and-effect relationship? Spot on! We don't have to dive deeper into this right now. Just keep in mind that in a cause-and-effect relationship, one event takes place before another, and the second event can't happen before the first. For instance, you must add or remove a menu item to see how it affects sales revenue.

Experimental Research Example

Suppose you start an ice cream stall in the summer and decide to try out different ice cream flavors to see which one performs best. In this case, the ice cream flavor is the independent variable, and the ice cream sale revenue affected by the change of flavor is a dependent variable.

Note here that the independent variable is the cause while the dependent variable is the effect . It is the change in the ice cream flavor that leads to the change in ice cream sales. While you can control which ice cream flavor to sell, you can't know which flavor will bring you the most profit without experimenting. This brings us back to a point early on: A cause can be manipulated to test or measure the effect.

Types of Experimental Research

When it comes to experimental research, there are three main types: controlled, manipulated, and random.

Controlled experimental research - Research where all outside factors are kept constant. Only the measured variable is changed. For example, a hamburger store changes its packaging while keeping everything else (ingredients, flavor, etc.) the same to observe the effect of the new packaging on sales.

Manipulated experimental research - Research where you can change the independent variable to measure the effect on the dependent variable. For instance, a bakery changes the amount of flour in bread and sees how customers respond.

Random experimental research - A combination of the above two. For example, you add a new drink to the menu of all coffee stores you own, with everything remaining the same. If you randomly check a store, you will see that the sales may go up or down depending on the location.

The experimental research method may be different, but the idea is always the same, to find out the strategy that helps the business improve its performance.

Descriptive vs Correlational vs Experimental Research

Have you heard of descriptive and correlational research before? If you have, you might also know that they are human behavior research methods just like experimental research. Don't worry if they can't tell the difference between them. This section will help clearly distinguish these three types of research methods.

Let's start with the definitions:

Descriptive research is the type of research that provides an accurate description of a situation. As a result, it is often used to discover information, make predictions, and test a hypothesis. The only drawback is that it does not explain the relationship between different variables.

Correlational research is also used to test hypotheses and make predictions. However, unlike descriptive research, it allows the researcher to observe the causal relationship between variables.

Experimental research is similar to correlational research but goes one step further by letting the researcher manipulate the variable to see different results. 1

Here's a more detailed comparison table:

Table 2. Descriptive vs Correlational vs Experimental Research. Source: Openpress.

Difference between Correlational and Experimental Research

Correlational research shows only the association between two variables, not necessarily the cause-and-effect relationship between them. Experimental research, on the other hand, allows the marketer to manipulate the independent variable to measure its effect on the dependent variable.

Correlational research only answers the question: Is there a positive or negative correlation between two variables? Experimental research goes one step further by measuring the impact of this correlation.

Suppose a restaurant wants to observe the effect of a new sauce recipe on sales. If using correlational research , the restaurant owner can only observe the relationship between one type of sauce and sales at a time. With experimental research , the owner can try out different types of sauce for a week and see which one drives the most sales and adds it to the menu.

Quasi-Experimental Research

Marketers use quasi-experimental research to understand the change in customer or firm behavior. According to Campell, this is the type of research where the " data generating process is not intentionally experimental" but caused by an external shock. 6

In quasi-experimental research , an external shock causes a variation that the researcher will use to study its impact on a situation.

Quasi-experimental research of eBay : The company paused advertising on Bing and saw little traffic loss. This inspired a follow-up experiment where the company randomly stopped paid search advertising and discovered similar results. In this example, the Quasi-experiment was used to study the consequence of the company's actions. 7

Experimental Research - Key Takeaways

• Experimental research conducts experiments to determine which marketing activity appeals to customers.

Experimental research is based on actual data and real-life situations rather than guesswork by the researcher.

Besides experimental research, there are two other approaches to studying human behavior - descriptive and correlational research.

Experimental research differs from other types of research in that it studies the causal relationship between independent and dependent variables.

Quasi-experimental research involves researching the impact of a variation caused by an external shock on a situation. Marketers use quasi-experimental research to understand the change in customer or firm behavior.

• Charles Stangor & Jennifer Walinga, Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour, n.d., https://openpress.usask.ca/introductiontopsychology/chapter/psychologists-use-descriptive-correlational-and-experimental-research-designs-to-understand-behavior/
• Know This, Descriptive Market Research, n.d., https://www.knowthis.com/planning-for-marketing-research/descriptive-market-research/
• Know This, Causal Market Research, n.d., https://www.knowthis.com/planning-for-marketing-research/causal-market-research/
• Stangor, Research methods for the behavioural sciences (4th ed.), 2011.
• Pritha Bhandari, Independent vs. Dependent Variables | Definition & Examples, 2022.
• Campbell, Roy H., A Managerial Approach to Advertising Measurement, Journal of Marketing, 1965.
• Blake, Thomas, Nosko, Chris, Tadelis, Steven, Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment, Econometrica, 83 (1), 155–74, 2015.
• Tanya Castaneda, How Successful Companies Spend Their Advertising Budget, 2018, https://mediamaxnetwork.com/blog/how-successful-companies-spend-their-advertising-budget/.

Flashcards in Experimental Research 14

Descriptive research method

Correlational research 

an external shock

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Frequently Asked Questions about Experimental Research

What is experimental research?

Experimental research involves testing and analysing marketing variables changes to determine which marketing activity will appeal to customers. 

What is quasi-experimental research?

Quasi-experimental research is the research on how a variable caused by an external shock should impact the firm's performance. It is mainly used to understand the consequences of customer or firm behaviour changes. 

What is the purpose of experimental research?

The purpose of experimental research is to find out which marketing activity will have the most positive impact on the business. It also helps marketers learn about customers' needs and develop offerings that match their expectations. 

What are the types of experimental research?

Experimental research can be controlled, manipulated, or random. The controlled experimental research is conducted where all outside factors are kept the same. Only the measured variable is changed. The manipulated experimental research involves changing the variable to obtain the desired results. Random experimental research is the combination of the above two. 

What is the difference between correlational and experimental research?

Correlational research shows only the association between two variables, not necessarily the cause and effect relationship between them. In experimental research, you can manipulate the independent variable to measure its effect on the dependent variable. In correlational research, you must keep everything as it is.

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